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strict\";function r(t){this._root=t,this._startText=this.getText(),this._fallbackText=null}var i=n(3),o=n(18),a=n(172);i(r.prototype,{destructor:function(){this._root=null,this._startText=null,this._fallbackText=null},getText:function(){return\"value\"in this._root?this._root.value:this._root[a()]},getData:function(){if(this._fallbackText)return this._fallbackText;var t,e,n=this._startText,r=n.length,i=this.getText(),o=i.length;for(t=0;t<r&&n[t]===i[t];t++);var a=r-t;for(e=1;e<=a&&n[r-e]===i[o-e];e++);var u=e>1?1-e:void 0;return this._fallbackText=i.slice(t,u),this._fallbackText}}),o.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";var r=n(21),i=r.injection.MUST_USE_PROPERTY,o=r.injection.HAS_BOOLEAN_VALUE,a=r.injection.HAS_NUMERIC_VALUE,u=r.injection.HAS_POSITIVE_NUMERIC_VALUE,c=r.injection.HAS_OVERLOADED_BOOLEAN_VALUE,s={isCustomAttribute:RegExp.prototype.test.bind(new RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"),null!=e.dangerouslySetInnerHTML&&(null!=e.children&&g(\"60\"),\"object\"==typeof e.dangerouslySetInnerHTML&&z in e.dangerouslySetInnerHTML||g(\"61\")),null!=e.style&&\"object\"!=typeof e.style&&g(\"62\",r(t)))}function o(t,e,n,r){if(!(r instanceof D)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===q,u=o?i._node:i._ownerDocument;B(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;k.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;N.postMountWrapper(t)}function c(){var t=this;O.postMountWrapper(t)}function s(){var t=this;A.postMountWrapper(t)}function l(){L.track(this)}function f(){var t=this;t._rootNodeID||g(\"63\");var e=j(t);switch(e||g(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "from catboost import CatBoostClassifier, Pool\n",
    "import shap\n",
    "from scipy.interpolate import interp1d\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.5)\n",
      "train.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
      "test.csv: Skipping, found more recently modified local copy (use --force to force download)\n",
      "gender_submission.csv: Skipping, found more recently modified local copy (use --force to force download)\n"
     ]
    }
   ],
   "source": [
    "!kaggle competitions download -c titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape, test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all=pd.concat([train_data[['PassengerId','Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']],\n",
    "                 test_data[['PassengerId','Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']]\n",
    "                ]).set_index('PassengerId')\n",
    "y_train=train_data[['PassengerId','Survived']].set_index('PassengerId')['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "PassengerId                                                      \n",
       "1                 3    male  22.0      1      0   7.2500        S\n",
       "2                 1  female  38.0      1      0  71.2833        C\n",
       "3                 3  female  26.0      0      0   7.9250        S\n",
       "4                 1  female  35.0      1      0  53.1000        S\n",
       "5                 3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_columns=['Age','SibSp','Parch','Fare']\n",
    "cat_columns=list(filter(lambda x:x not in num_columns,X_all.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categorical features: finn nan's with string\n",
    "X_all[cat_columns]=X_all[cat_columns].astype(str)\n",
    "X_all[cat_columns]=X_all[cat_columns].fillna('NAN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data for catboost\n",
    "X_train=X_all.iloc[:len(train_data),:]\n",
    "X_test=X_all.iloc[len(train_data):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data for logreg\n",
    "X_train_lor=X_train.copy()\n",
    "for col in num_columns:\n",
    "    X_train_lor[col]=X_train_lor[col].fillna(X_train_lor[col].median())\n",
    "X_train_lor=pd.get_dummies(X_train_lor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trn, X_val, y_trn, y_val = train_test_split(X_train, \n",
    "                                              y_train, \n",
    "                                              test_size=.2, \n",
    "                                              random_state=4321)\n",
    "X_trn_lor = X_train_lor.loc[X_trn.index,:]\n",
    "X_val_lor = X_train_lor.loc[X_val.index,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lor = LogisticRegression(random_state=0, solver='lbfgs',max_iter=500).fit(X_trn_lor,y_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.8156424581005587\n"
     ]
    }
   ],
   "source": [
    "print('accuracy:',accuracy_score(y_val,lor.predict(X_val_lor)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "            background: orangered;\n",
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       "            background: orangered;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow3_col0 {\n",
       "            background: lightgreen;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow4_col0 {\n",
       "            background: lightgreen;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow5_col0 {\n",
       "            background: orangered;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow6_col0 {\n",
       "            background: orangered;\n",
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       "            background: lightgreen;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow8_col0 {\n",
       "            background: orangered;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow9_col0 {\n",
       "            background: lightgreen;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow10_col0 {\n",
       "            background: orangered;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow11_col0 {\n",
       "            background: orangered;\n",
       "        }    #T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow12_col0 {\n",
       "            background: lightgreen;\n",
       "        }</style><table id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bd\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >exp(β)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row0\" class=\"row_heading level0 row0\" >Age</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow0_col0\" class=\"data row0 col0\" >0.961</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row1\" class=\"row_heading level0 row1\" >SibSp</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow1_col0\" class=\"data row1 col0\" >0.693</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row2\" class=\"row_heading level0 row2\" >Parch</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow2_col0\" class=\"data row2 col0\" >0.912</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row3\" class=\"row_heading level0 row3\" >Fare</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow3_col0\" class=\"data row3 col0\" >1.003</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row4\" class=\"row_heading level0 row4\" >Pclass_1</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow4_col0\" class=\"data row4 col0\" >2.55</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row5\" class=\"row_heading level0 row5\" >Pclass_2</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow5_col0\" class=\"data row5 col0\" >0.992</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row6\" class=\"row_heading level0 row6\" >Pclass_3</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow6_col0\" class=\"data row6 col0\" >0.396</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row7\" class=\"row_heading level0 row7\" >Sex_female</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow7_col0\" class=\"data row7 col0\" >3.677</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row8\" class=\"row_heading level0 row8\" >Sex_male</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow8_col0\" class=\"data row8 col0\" >0.272</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row9\" class=\"row_heading level0 row9\" >Embarked_C</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow9_col0\" class=\"data row9 col0\" >1.319</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row10\" class=\"row_heading level0 row10\" >Embarked_Q</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow10_col0\" class=\"data row10 col0\" >0.821</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row11\" class=\"row_heading level0 row11\" >Embarked_S</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow11_col0\" class=\"data row11 col0\" >0.755</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdlevel0_row12\" class=\"row_heading level0 row12\" >Embarked_nan</th>\n",
       "                        <td id=\"T_562e7bd2_ddd4_11e9_a117_34e12dd4d5bdrow12_col0\" class=\"data row12 col0\" >1.224</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x251398f8c50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lor_coef = pd.Series(lor.coef_[0], index = X_trn_lor.columns)\n",
    "lor_coef = pd.DataFrame({'exp(β)':lor_coef.apply(lambda x:np.exp(x))}).round(3)\n",
    "lor_coef.style.apply(lambda x: [\"background:orangered\" if v<1 else \"background:lightgreen\"\n",
    "                                for v in x], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_features=[X_trn.columns.to_list().index(col) for col in cat_columns]\n",
    "cat = CatBoostClassifier(cat_features=cat_features,silent=True).fit(X_trn,y_trn)\n",
    "probas_cat = pd.Series(cat.predict_proba(X_all)[:,1],index=X_all.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.8715083798882681\n"
     ]
    }
   ],
   "source": [
    "print('accuracy:',accuracy_score(y_val,cat.predict(X_val)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fuctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shap2deltaprob(features, \n",
    "                   shap_df, \n",
    "                   shap_sum,\n",
    "                   probas,\n",
    "                   func_shap2probas):\n",
    "    '''\n",
    "    map shap to Δ probabilities\n",
    "    --- input ---\n",
    "    :features: list of strings, names of features\n",
    "    :shap_df: pd.DataFrame, dataframe containing shap values\n",
    "    :shap_sum: pd.Series, series containing shap sum for each observation\n",
    "    :probas: pd.Series, series containing predicted probability for each observation\n",
    "    :func_shap2probas: function, maps shap to probability (for example interpolation function)\n",
    "    --- output ---\n",
    "    :out: pd.Series or pd.DataFrame, Δ probability for each shap value\n",
    "    '''\n",
    "    # 1 feature\n",
    "    if type(features) == str or len(features) == 1:\n",
    "        return probas - (shap_sum - shap_df[features]).apply(func_shap2probas)\n",
    "    # more than 1 feature\n",
    "    else:\n",
    "        return shap_df[features].apply(lambda x: shap_sum - x).apply(func_shap2probas)\\\n",
    "                .apply(lambda x: probas - x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def partial_deltaprob(feature, X, shap_df, shap_sum, probas, func_shap2probas,\n",
    "                      cutoffs = None\n",
    "                     ):\n",
    "    '''\n",
    "    return univariate analysis (count, mean and standard deviation) of shap values based on the original feature\n",
    "    --- input ---\n",
    "    :feature: str, name of feature\n",
    "    :X: pd.Dataframe, shape (N, P)\n",
    "    :shap_df: pd.DataFrame, shape (N, P)\n",
    "    :shap_sum: pd.Series, series containing shap sum for each observation\n",
    "    :probas: pd.Series, series containing predicted probability for each observation\n",
    "    :func_shap2probas: function, maps shap to probability (for example interpolation function)\n",
    "    :cutoffs: list of floats, cutoffs for numerical features\n",
    "    --- output ---\n",
    "    :out: pd.DataFrame, shape (n_levels, 3)\n",
    "    '''\n",
    "    dp_col = shap2deltaprob(feature, shap_df, shap_sum, probas, func_shap2probas)\n",
    "    dp_col_mean = dp_col.mean()\n",
    "    dp_col.name = 'DP_' + feature\n",
    "    out = pd.concat([X[feature], dp_col], axis = 1)\n",
    "    if cutoffs:\n",
    "        intervals = pd.IntervalIndex.from_tuples(list(zip(cutoffs[:-1], cutoffs[1:])))\n",
    "        out[feature] = pd.cut(out[feature], bins = intervals)\n",
    "        out = out.dropna()   \n",
    "    out = out.groupby(feature).describe().iloc[:, :3]\n",
    "    out.columns = ['count', 'mean', 'std']\n",
    "    out['std'] = out['std'].fillna(0)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shap on Catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_df = cat.get_feature_importance(data = Pool(X_all, cat_features=cat_features), \n",
    "                                  type = 'ShapValues')\n",
    "shap_df = pd.DataFrame(shap_df[:,:-1], columns = X_all.columns, index = X_all.index)\n",
    "shap_sum = shap_df.sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "        }    #T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col6 {\n",
       "            background: orangered;\n",
       "        }</style><table id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bd\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Pclass</th>        <th class=\"col_heading level0 col1\" >Sex</th>        <th class=\"col_heading level0 col2\" >Age</th>        <th class=\"col_heading level0 col3\" >SibSp</th>        <th class=\"col_heading level0 col4\" >Parch</th>        <th class=\"col_heading level0 col5\" >Fare</th>        <th class=\"col_heading level0 col6\" >Embarked</th>    </tr>    <tr>        <th class=\"index_name level0\" >PassengerId</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdlevel0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow0_col0\" class=\"data row0 col0\" >-0.36</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow0_col1\" class=\"data row0 col1\" >-0.76</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow0_col2\" class=\"data row0 col2\" >0.11</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow0_col3\" class=\"data row0 col3\" >0.05</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow0_col4\" class=\"data row0 col4\" >-0.03</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow0_col5\" class=\"data row0 col5\" >-0.3</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow0_col6\" class=\"data row0 col6\" >-0.08</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdlevel0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow1_col0\" class=\"data row1 col0\" >1.22</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow1_col1\" class=\"data row1 col1\" >2.18</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow1_col2\" class=\"data row1 col2\" >0.1</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow1_col3\" class=\"data row1 col3\" >0.06</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow1_col4\" class=\"data row1 col4\" >0.1</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow1_col5\" class=\"data row1 col5\" >0.78</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow1_col6\" class=\"data row1 col6\" >0.42</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdlevel0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow2_col0\" class=\"data row2 col0\" >-0.75</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow2_col1\" class=\"data row2 col1\" >1.62</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow2_col2\" class=\"data row2 col2\" >0.07</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow2_col3\" class=\"data row2 col3\" >0.1</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow2_col4\" class=\"data row2 col4\" >-0.02</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow2_col5\" class=\"data row2 col5\" >-0.01</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow2_col6\" class=\"data row2 col6\" >-0.13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdlevel0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow3_col0\" class=\"data row3 col0\" >1.15</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow3_col1\" class=\"data row3 col1\" >2.07</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow3_col2\" class=\"data row3 col2\" >0.11</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow3_col3\" class=\"data row3 col3\" >0.05</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow3_col4\" class=\"data row3 col4\" >0.08</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow3_col5\" class=\"data row3 col5\" >0.89</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow3_col6\" class=\"data row3 col6\" >-0.13</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdlevel0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col0\" class=\"data row4 col0\" >-0.41</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col1\" class=\"data row4 col1\" >-0.73</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col2\" class=\"data row4 col2\" >-0.06</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col3\" class=\"data row4 col3\" >0.08</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col4\" class=\"data row4 col4\" >-0.03</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col5\" class=\"data row4 col5\" >-0.15</td>\n",
       "                        <td id=\"T_696242dc_ddd4_11e9_9e18_34e12dd4d5bdrow4_col6\" class=\"data row4 col6\" >-0.08</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x251399e1358>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show shap\n",
    "temp = shap_df.head().round(2)\n",
    "temp.style.apply(lambda x:[\"background:orangered\" if v<0 else \"background:lightgreen\"\n",
    "                           for v in x], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build interpolation function to map shap into probability\n",
    "shap_sum_sort = shap_sum.sort_values()\n",
    "probas_cat_sort = probas_cat[shap_sum_sort.index]\n",
    "\n",
    "intp = interp1d(shap_sum_sort,\n",
    "                probas_cat_sort, \n",
    "                bounds_error = False, \n",
    "                fill_value = (0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col0 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col1 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col2 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col3 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col4 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col5 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col6 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col0 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col1 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col2 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col3 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col4 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col5 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col6 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col0 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col1 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col2 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col3 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col4 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col5 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col6 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col0 {\n",
       "            background: lightgreen;\n",
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       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col2 {\n",
       "            background: lightgreen;\n",
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       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col4 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col5 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col6 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col0 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col1 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col2 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col3 {\n",
       "            background: lightgreen;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col4 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col5 {\n",
       "            background: orangered;\n",
       "        }    #T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col6 {\n",
       "            background: orangered;\n",
       "        }</style><table id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bd\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Pclass</th>        <th class=\"col_heading level0 col1\" >Sex</th>        <th class=\"col_heading level0 col2\" >Age</th>        <th class=\"col_heading level0 col3\" >SibSp</th>        <th class=\"col_heading level0 col4\" >Parch</th>        <th class=\"col_heading level0 col5\" >Fare</th>        <th class=\"col_heading level0 col6\" >Embarked</th>    </tr>    <tr>        <th class=\"index_name level0\" >PassengerId</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdlevel0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col0\" class=\"data row0 col0\" >-4.48%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col1\" class=\"data row0 col1\" >-10.88%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col2\" class=\"data row0 col2\" >+1.11%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col3\" class=\"data row0 col3\" >+0.58%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col4\" class=\"data row0 col4\" >-0.33%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col5\" class=\"data row0 col5\" >-3.71%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow0_col6\" class=\"data row0 col6\" >-0.88%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdlevel0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col0\" class=\"data row1 col0\" >+3.09%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col1\" class=\"data row1 col1\" >+9.54%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col2\" class=\"data row1 col2\" >+0.14%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col3\" class=\"data row1 col3\" >+0.09%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col4\" class=\"data row1 col4\" >+0.14%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col5\" class=\"data row1 col5\" >+1.57%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow1_col6\" class=\"data row1 col6\" >+0.7%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdlevel0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col0\" class=\"data row2 col0\" >-16.65%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col1\" class=\"data row2 col1\" >+36.41%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col2\" class=\"data row2 col2\" >+1.61%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col3\" class=\"data row2 col3\" >+2.38%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col4\" class=\"data row2 col4\" >-0.53%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col5\" class=\"data row2 col5\" >-0.3%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow2_col6\" class=\"data row2 col6\" >-3.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdlevel0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col0\" class=\"data row3 col0\" >+5.08%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col1\" class=\"data row3 col1\" >+14.69%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col2\" class=\"data row3 col2\" >+0.28%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col3\" class=\"data row3 col3\" >+0.14%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col4\" class=\"data row3 col4\" >+0.2%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col5\" class=\"data row3 col5\" >+3.42%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow3_col6\" class=\"data row3 col6\" >-0.3%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdlevel0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col0\" class=\"data row4 col0\" >-5.17%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col1\" class=\"data row4 col1\" >-10.26%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col2\" class=\"data row4 col2\" >-0.64%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col3\" class=\"data row4 col3\" >+0.81%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col4\" class=\"data row4 col4\" >-0.34%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col5\" class=\"data row4 col5\" >-1.75%</td>\n",
       "                        <td id=\"T_696b7eae_ddd4_11e9_94fd_34e12dd4d5bdrow4_col6\" class=\"data row4 col6\" >-0.92%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2513aaac6d8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show Δ probabilities\n",
    "temp = shap2deltaprob(X_all.columns.to_list(),\n",
    "                      shap_df,\n",
    "                      shap_sum,\n",
    "                      probas_cat,\n",
    "                      func_shap2probas=intp).head()\\\n",
    "        .applymap(lambda x:('+'if x>0 else '')+str(round(x*100,2))+'%')\n",
    "\n",
    "temp.style.apply(lambda x: [\"background:orangered\" if float(v[:-1])<0 else \"background:lightgreen\"\n",
    "                                for v in x], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(0,intp(0),s=200,fc='red',ec='black',zorder=3,label='Observation considering all features\\nexcept Passenger Age')\n",
    "plt.scatter(2,intp(2),s=200,fc='lime',ec='black',zorder=3,label='Observation considering all features')\n",
    "\n",
    "plt.annotate(s='',xy=(2-.5,intp(2)+.5*.18),xytext=(0-.5,intp(0)+.5*.18),arrowprops={'fc':'black'})\n",
    "plt.text(-1.5,.72,'Marginal effect of\\nPassenger Age',ha='center',va='center',fontsize=13)\n",
    "\n",
    "plt.annotate(s='',xy=(2,intp(0)-.5*.18),xytext=(0,intp(0)-.5*.18),arrowprops={'fc':'black'})\n",
    "plt.text(1,.23,'Δ Shap sum\\n(= +2)',ha='center',va='top',fontsize=13)\n",
    "\n",
    "plt.annotate(s='',xy=(2+.5,intp(2)),xytext=(2+.5,intp(0)),arrowprops={'fc':'black'})\n",
    "plt.text(4,(intp(0)+intp(2))/2,'Δ Predicted\\n Probability\\n(= +44%)',va='center',ha='center',fontsize=13)\n",
    "\n",
    "legend = plt.legend(bbox_to_anchor=(0.5, -.2), loc = 'upper center',fontsize=13)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('lightyellow')\n",
    "frame.set_edgecolor('black')\n",
    "\n",
    "plt.scatter(shap_sum_sort, probas_cat_sort,color='blue',zorder=2)\n",
    "\n",
    "plt.gca().set_facecolor('lightyellow')\n",
    "plt.grid(zorder=1)\n",
    "plt.xlabel('SHAP sum',fontsize=13)\n",
    "plt.ylabel('Predicted Probability\\nof Survival',fontsize=13)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.yticks([0,.25,.5,.75,1],['0%','25%','50%','75%','100%'],fontsize=13)\n",
    "plt.title('From SHAP to Predicted Probability',fontsize=15)\n",
    "\n",
    "plt.savefig('shap2probability_example.png',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex         0.339945\n",
       "Pclass      0.166060\n",
       "Fare        0.163518\n",
       "Age         0.158419\n",
       "SibSp       0.067203\n",
       "Parch       0.058750\n",
       "Embarked    0.046104\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature importance\n",
    "pd.Series(cat.feature_importances_, index=cat.feature_names_).sort_values(ascending=False) / sum(cat.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp = partial_deltaprob('Sex', X_all, shap_df, shap_sum, probas_cat, func_shap2probas=intp)\n",
    "\n",
    "plt.plot([0,len(dp)-1],[0,0],color='dimgray',zorder=3)\n",
    "plt.plot(range(len(dp)), dp['mean'], color = 'red', linewidth = 3, label = 'Avg effect',zorder=4)\n",
    "plt.fill_between(range(len(dp)), dp['mean'] + dp['std'], dp['mean'] - dp['std'],\n",
    "                 color = 'lightskyblue', label = 'Avg effect +- StDev',zorder=1)\n",
    "yticks = list(np.arange(-.2,.41,.1))\n",
    "plt.yticks(yticks, [('+' if y > 0 else '') + '{0:.1%}'.format(y) for y in yticks], fontsize=13)\n",
    "plt.xticks(range(len(dp)), dp.index, fontsize=13)\n",
    "plt.ylabel('Effect on Predicted\\nProbability of Survival',fontsize=13)\n",
    "plt.xlabel('Sex', fontsize=13)\n",
    "plt.title('Marginal effect of\\nSex', fontsize=15)\n",
    "plt.gca().set_facecolor('lightyellow')\n",
    "plt.grid(zorder=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q5lHLFp5ddz9wfz197wHu8WGOx2opexJ40kcz2wyVCChsKogirzCSlKhKJmQV0i+5FIc55zIYDEFE0DksQ1OxzrkOlDr4YHsy83YoYzOdDEsvJtqccxkMhiDAOKwQxOV24AI+35PA53sSGJJazJjMIpKj2vQup8FgCHGMwwphKtSKzPVdfhyrD8XRLb6M8VlOusSVG1m8wWBocxiHZcBtn3NtLYxiZ1EkCRGVTMhyMiClhDDjuAwGQxsh6ILfGvyJ4HI7OFQWwcc7kvjnmiy+2BtPaYXxWgaDIfCYFZahVjznXF/sjeeLvQkMTClmbKaT1GhzzmUwGAKDcViGevGcc605FMsPh2PpFFfOhKxCusWbcy6DwdC6GIdl8AlPmpPtzij2bIkgLtzN+KxCBqaUEG42lg0GQytgHJah0bjcDo6UO1iwM4lPdyYxKsPJyPRiYiNM+CeDweA/jMMyNBmX21paLdsXz7J9CQxILmFcppP0GJPmxGAwtDzGYRmaTaV9zvXD4RjWH4kmM9bFhCwnPRNMmhODwdByGIdlaDE8aU52FUXx7tYIYsLcjMtyMji1mAhzzmUwGJqJcVgGv2ClOXHw2a5EFu5K5IQORYzqUES8OecyGAxNxDgsg1/xnHN9vT+er/fH0zeplHGZhWTGmnMug8HQOIzDMrQKlWodZq0/Es2mo1Gkx1QwIauQPonmnMtgMPiGcViGVsVzzrW3OJI5eSlEhSnjMgsZklpCpElzYjAY6sE4LEPAsM65IGdXIjm7ExmeVsyJGU4SI805l8FgOB7jsAwBx6UOUPj2YBzfHoyjV2Ip47OcdIx1Bdo0g8HQhvCLwxKRy31pp6qv+2N+Q3DiOefaeDSarQVRpEZXMiGrkL5JpTjMOZfBEPL4a4X1oA9tFDAOy1AL1jnX/hIHc7clE+FQxmY4GZZeTJQ55zIYQha/OCxV7emPcQ2hh+eca/HeBBbvSWBIWjFjMopIjjJpTgyGUMOcYRmCggr7Ptd3B+NYnR9H9/gyxmcV0jnOZWTxBkOI4HeHJSIC/BQ4HegAVH29qOpp/p7f0L5wI6CwpTCKHUWRJEZUMj6rkAEppYQZx2UwtGtaI8Lbg8CfgB3AWOAbYCCwqhXmNrRbBJfbQX5ZBB/vSOafa7L4cl8cpRXGaxkM7ZXWcFiXA1NU9R6g3P55IdCjpScSkdtEZLmIFIvIpjraXCUim+02y0VkpFddlojkiMhREXlPROK86saLyFciEtbSdldjz35QIyxoDC63g9JKB0v3JPDk91l8tD2Jw2X+/WcyGAytT2s4rFRV/c5+XikiYaq6DDi1KYOJSA8RqesbfTfwV+pQKYrIScDTwM+AFOBt4EMRSbSb/BpYAaQDbuBGu1+U3e+nquq/0363G048n8k/uZqT//V70jd977ep2iMV6qBChTX5sbyQm8EbG1PZVhhp/L/B0E5oDYe1S0S62c+3AGeLyFigxW+FqupsVX0b2FVHk+uBd1R1vqqWAX8DyoCL7Po+wAJVdQHz7dcA9wHvqerqlra5GktXwK69xO/exYQXHuanM07gJ9OGMeHZB0jNW+/XqdsTblsWv80ZzewtqTybm8Ga/BgqTQANgyGoaQ2V4NPASGA78A/gPSzhxX2tMHdNhgEve16oqorISrscYDVwlogsBCYD80VkBDAVGN3Q4CJyA3ADQGZmBjk5jXMymfO+om9sLOHFxVVlHbbk0uGZP3LyM3/kcK8+bJ84ie0TT6Uoq2Ojxg51ftgGuUBsuJvYCDcOQnfZ5XSWNvqzaTA0Bn99xkTr2C8Rkat8GUBVX2nUhCJdgHhVXdeYfl79ewBbVbXO03URuQb4rar2qVG+GXhAVV/yKpsJuFT1pyKSBPwTGAEsBH4FLAZuBjoCdwAV9tjL6rNz1KihumLF+41+f5SWsuzP/yVx+ff0WfwBkaXFtTbbPWgUuVNmsO7MaRRmdmn8PCFKuLhRhOzkEsZmOUmPDr00Jzk565k0qX+gzTC0Y5rzGRPp8Y2qjqqtrr4V1u9qvPZs6+0HMuzn24B6HZaI/BiYraqlAKq6s0GLjx/jKSzxBtjbmCJyxKvJw6r6sA9DFQJJNcqSgc22bUeBKkctIr8BcoCNwDtYK7EM4GMR6aF1efvmEB3N7gknkXPRvUSUFNF78Qdkz59F76UfE15eVtWs09oVdFq7gtP//gt2DB9P7uQZrD/jYorSs1rcpPZEhVq74GsPx7DuSAxZseVMyHLSI8GkOTEY2jp1OixV7et5LiK/wFL13a2qxbZ67q9Ang9z/BF4UkT+C7ygql811khVvRlrleO9wkpu7DjAd8AJnhf2HbHhWM6oGiKSDVwGnAgMAXaoaj6Qb4swOmA5b7/hiolj3ZQZrJsyg0hnAX1z5pA9fzY9ly0grOLYEWDXVV/QddUXnPHInew4YSI/TJnBhtMupCQl3Z/mBTVWmhPYWRTFO1sjiA13My7TyeDUYsJb42TXYDA0Gl/PsG4HetpCBVS1SETuxlqZ/K2+jqraS0ROBa4BPhORbcBLwCuq2qJf+CISjvWeIqyXEm3bUGo3eQ5rdTQT+By4DYgG3q0xjgN4HrhFVUtsm/vb4pEMe/z8lrS9IcrjE1l77o9Ze+6PiT56iH4L/0f2/Fl0/3ohjkpLuOhwu+m+IofuK3KY8vCt5I0+ndzJ09lw2gWUJTTFv4cGLreDo+UOPt2VyGe7EhnZoYhRHYqIizAqDYOhLeGrwwoDOgFbvco6+tpfVRcCC0Xk58AMLGHCg0CU76b6xG+pLuYosX+KbccSEbkZy3F1BNYAU1W1oMY4twGrVTXH7rdPRH4PfA2U4295ewOUJqWy+sJrWX3htcQe2k//T98le/4sun77OWLvUjoqK+n15Xx6fTmfKX/+OVvHnUnu5OlsOuU8yuMSAmV6m8Zlh3/6an8cX+2Pp19SCeOynGTEhN45l8HQFqlTdFGtkcjfsZRyD2OdW/UA7gHmqeodPk8mMhy4Fus8qlhVuzfB5qCgyaIL4J15m9mQcXKj+8Uf2E3/T94he95bdFlduybEFRXN5pPOJnfydDafNJWKmNgm2RgKCEqYKB1iKpiQVUjvxPZxzmVEFwZ/EwjRhTe/AA5jXaztgnXP6VXgoYYnlzTgCuA6oB/W9tvlwCc+zm3wEWeHTnxz2S18c9ktJO7exoBP3iZ73lt0zP22qk1EWSkDPn2XAZ++S3lMHBtPOZd1k6ezZfwUKiNbesEb3Kh9n2tPcST/y0shKkwZn1nIkLQSIhyhK4s3GAKFTyusZk0gUgasxLr/9IatxGv3BGKFVRfJ2zeRvWA22fPeIqOO6Bml8YlsPPUCcidPJ2/06bgjIlps/vZEhMM61xqRVsyJGU4SIoPvnMussAz+JtArLOw7SucAnVX1byKSBThUdXcDXU9Q1bW+m2toaY5068OXP/kVX/7kV6RtySV7/iwGzJ9Fulf0jGhnAUPef5Uh779KSVIq60+7kNzJM9g+ciIabrLQePCcc604GMc3B+PolVjKhCwnWbEtHrjFYDDUwKdvIhE5AZgH7AF6YikDh2LF2rukvr7GWbUt8ntls+Sm37Pkxt+RsXE1A+bNInvBbFJ2bqlqE3P0EMPffZHh775IUWoG606/mNwpM9g5fDw4jOYbwG3fW994NJqtBVGkRVcwIctJn6RSHO3gnMtgaIv4+qfz48AvVPUlETlsl32BJU8/DhE5pKqp9nMX1B4HR1UjG2mvoaUQYX+/YezvN4zFt/yJrNxvyZ73FgMWzCZp746qZnGH9jNy1jOMnPUMBRmdWX/GJfwwZQZ7Bp9Iu1AgNBvrnGtfSSTvb0sm0qGMzXQyNK2YqDBzzmUwtCS+OqxBHIvBpwCq6vROv1GD872en0kdDsvQRhBh78CR7B04koX/9xCd1yy3nNcnbxN/cG9Vs8T9uzjx9Sc48fUnONKpB+vOvITcyTPYN2C4cV5Y24UuNyzancCi3QkMSytmdGYRSZEBuwFhMLQrfHVYB7BCM23zFIhIH+qIiq6qS7ye5zTDPkNr43Cwa9g4dg0bx6d3PULXlUvInvcW/T99l9gjB6uaJe/OY+zMRxk781EOdetD7uTp5E6ezsE+gwNofNvAE/5p5cE4VuXH0SOhjPFZhXSOM+dcBkNz8NVhzQTeFJF7sCJIjAQexbqAWy92IsUXgZmqWlfaD0MbRMPC2D7qFLaPOoX5v3yc7l8vJHv+LPp/9h7RhcdCOaZu38SE5x9iwvMPcaBXNrlTZpA7eTqHu/cLoPWBx42AwuaCKLY7I0mKrGR8ZiEDUsw5l8HQFHw9Qf8LVvTyD7GCxy7ECm30Tx/6PoiVqmOriHwkItNFxGimgwwNDydv3Jl8dN+zPPHJTmY99i7fT72cshpRMzpsyWXi03/gxosGc+1lJzLm5b+RtGtrHaOGCoLL7eBgaQQf7Ujmn2syWbYvjtJK47UMhsbQ6HtYIpKuqgcbbnlcv95Y8QSvBOKB11T1/xo7TrDQlu5h+ZPw0hJ6Lf2Y7Pmz6PP5B0SUltTabvfgE8mdPIN1Z15i0qFgpTkBYXBqMWMynaREtd45l7mHZfA3/rqH5Wtopo9V9axayj9Q1XMaZ4xkAS8AZ6lqWGP6BhOh4rC8iSh20udzKx1Kr6XzqqVD8WbH8AnkTp5u0qEADhQR6BpXxoSOTrrElftdv2IclsHfBPri8Pg6ysf6ZoCEAedixRE8C/gG6w6XoR3hio0nd8qPyJ3yI6IKj9J30RwGVKVDORZAtuuqpXRdtZQzHrmT7SNPsSPKh2Y6FM85V54zil2bI0mIqGR8ppPslBLCzJU3g6Ea9TosEfEkTQwXkcuwo57b9MWKL1gvduDcK7CinP8H6z7XhqaZawgWyhKS+P7cK/n+3CuPpUOZ95aVDsVthTNyuN30+HohPb5eyOS/3Ebe6NNZN3k6G049PwTToQgut3CozMG8nUl8siuJEzs4OaFDETHh5laIwQANr7AetH9GAX/2KncDe4FbfZijK9bZ1TxVDb7Aa4Zm40s6lLCKCnp/MY/eX8xjyoORbB0/mdzJ09l4ynm4YuMD/A5aF5fbgQv4cl88X+5LIDulhLGZhaRFm/tchtCmXoelqj0BRGSOqp5fX9vasBMqxgILjbMyABSnZrBy+o2snH4j8Qd2M2DB2wyYP6taOpRwVzl9F82l76K5VjqUk6da6VAmnB1S6VA897m+PxRD7uEYOsaWM6FjId3j/X/OZTC0RXw9w7paROJUtchTYEe5iFDVI3V1UtUK+86WyYBnOA5nh06suPxWVlx+q5UOZcFssufPOj4dyifvMOCTd6rSoeROmcHWcZNDJh2KleYEdhRF8faWCGLD3YzPLGRQagnh5pzLEEL4+nGfA9QMYTAY+J8PfV8FbmmMUYbQo6BTd766+i5mvraMZ977gUU3/4H9NaJmRJYUMejj/zLtjku49YwunHPfT+i5dB4OV+hEkHC5HRwtD+eTXUk8sSaLxbvjKXIZr2UIDXyVtR8G0ry39UTEARz0BLmtp++nwMnAdiAP6/wLAFWd3DSz2z6hKGv3B2lbfiB73iyyF8wiLa92rU5xchobTr2Q3Cl2OpSwdntb4jjC7Ptc/ZNLGJfppENMw5sZRtZu8DeBlrWXYJ1FOb3K4gFf/rRdbD8MhkaT32sgS352H0tu+j0ZG1eTPe8tsufPJtkrekbskXyGv/sCw999AWdaJutPv5jcydNDIh1KpX3OlXs4hg1HYsiIcTEhq5BeiWXmnMvQ7vDVYS0B/iwit6uqW0QE+COwtKGOqvqH5hhoMADV0qEsuuUBsn74huz5s8ieP4vEfTurmsXn72PkW08z8q2nKcjozLozp5E7eXq7T4fiOefaXRzJe3kpRIe5GZ/pZHBaCREOI4s3tA983RLsDnwGRANbsJI4lgOnqWpeA33runSMqn7RGGODCbMl2Eq43XRevczKorxgNvH5+2ptdqRTD3InT2Pd5Ons6x8a6VAiHNbu+wnpRZyYUUR8hPXabAka/E1AQzNZg0gMcB7QHess6gNVLfahX21ydk9OrXZ72GAcVusjlZV0/fZzK6L8p+8QeyS/1naHuvWpiih/sPegVray9Y7ltMUAACAASURBVAkT6/94n8RSxmUVsu6rtcZhGfxKwB1WSyEinYAHgLmq+k6rTt6KGIcVWBwuV1U6lH4L/1ctHYo3B3oPPJYOpVvfVraydRGUMFF67l3KwDGD6JNUSkT7PuIzBIhWd1gicreqPmI//3Vdg6vqn+uqq9sgSQC+VdV2+w1hHFbbIay8jJ5fLmDA/Fn0XfQ+UcXOWtvtHTDciig/eRpHO/VoXSNbkR57l7C703jcKvRJKmF4WjHdEspNji5DixEIleBpwCP28zPraKNUD9nkK1FARhP6NQsRyQHGUV3deKmqzrXrLwKeACKB+1X1aa++zwC5qvp461lsaAkqI6PYdMq5bDrlXK90KG/R5/MPq6VDyVq3iqx1qzj1iV+za/Bo1k2eTu7kaTgzOgfQev9Q7raWVuuOxLClIBoRGJhSzLC0EjJjXKFwxGcIQup0WKo61ev5qU2doJbVWRxwAbCgqWM2MF8PYKuq1vVf7k+q+kAt/cKwMiifgRXU91sReUdV94nIJGAIcLM/bDa0HhXRMWw4/SI2nH7RsXQo896y0qG4yqvadf7+Kzp//xWn/eMX7LTToaw742KK0zIDaL0/EMrd1n+VVQfj+P5QLNFhytC0IoaklpDcinm6DIaG8FXW3hxqrs6cwCzgH60wd2NIA1yqugpARDYC3UWkAHgSmG7iIbYvakuHkj1vFj2Wf1KVDkVU6bpyCV1XLuGMv93B9lGTrFxep11IaXJagN9By6J2xHiXG5btS2DZvgRSoioYkVZEdkopsRHm428ILPWdYW3FVvPVh6r2ammjmkN9Kyx7S3AwVkiqPVjpTh5RVZd9t2wH1urvEPAlMBD4DZDvy1mdiNwA3ACQmZkx8s03X23SezhSUEZpRGhFKG9LRBYcpcsXS+i2OIeM1Sur0qF44w4LY++IkWyfeCq7xk3AFRc8/15RLidljfh8ef4jRTiU2HA3UWFuzI6hoT6czlLi46Ob1PfUU6c0SXRxhdfLXljbYS8AW+3X1wJP1ba9Vhci0gfLYaxQ1Z0NtW8KDTisccA6oAA4EXgNeEtV77XrTwYewjrDegjYBTwFTMQ6qzsB2AzcpqqF9dlhRBftg9j8fcfSoaxcUpUOxZuKiEi2TJhC7uTpbJp4bptPh9Jj7xLysk5qUt8Ihxu3Cr0TSxmeXkyPhDIj1jAcR0Bl7SKyGLhTVVd4lZ0APKaqE+vo8ydgnaq+Zr++AGsrMBwoBs5W1c8b+2bqmOspwJNs0gEkAEe9mjysqg/X0u8Ku65rLXWRwFdYjnk0MF5VrxaRPwOqqr+pzybjsNof8ft3MWDB22TPn0XnNctrbeOKjmHzSWeTO2WGlQ4lOqaVrWyY5jisYyiRdgSNgSklDE0rpmOsEWsYLAIdS3A4sKpG2Wq7vC6mARd7vX4Y+BfwO+BX9s8WCX6rqjdjCyK8Vli+pKy1IofWzm+xLkevFJHrAU9UjsXAbc0yuAEE6y/ZCregZvOlzeDM6MyKK25jxRW3kbQ7jwHzrXQoWetWVrWJKC2pSodSFhvPplPOJXfyDLaOO7OdpUM5Jtb4Lj+WtYdjiHQow9KKGJJWQooRaxj8gK8Oaz1wB/A3r7LbgfpS3WfZ/RCRLkBf4GRVdYrIw1hba62GiCQDJwE5QBGWs70f+G8tbYdinWWNtos2A2eKyAvA2cAmf9qaHFXBJb0Osa0wiq0FURwojSBMlEoVKusUPxpak6OderD8mrtZfs3dpGzfaIWGmj+LjE1rq9pEFTsZ9NGbDProTUrjk9hw6gXkTpnBthNPxR0REUDrWxZvscby/Ql8tT+epMhKhqcXMzClhDgj1jC0EL5uCY4GPsRS+G3DCs8UD5yjqrXujYjIIaCDqlba24F/U9V+dp0AR1U1sWXeRrV5e1DLGZaIdADeB7I5Jrp4DXhIVcu92oVhCS5u98Q6FJF44HXgFGAl1t2tvfXZ0ZwtwZrL6Uo37C2JYHthFFsKothTHIEIqB7LSmtoG1SlQ5n/FmnbNtbapjg5jfWnXcS6ydMDkg6lZbYEGyZc3ChCVmw5I9KK6ZdcSmSYCcQbCgQ8NJOIJGLFEuyMJUaYq6pH62m/CPi3qr4uIq8Apap6g13XFViqqt0a91aCh5Z0WDVRhQOl4ewojGRzQTS7iiOpdFvxXF1u48DaBKpkbPjOjihfPR2KN870rGPpUIaNa5V0KK3lsLzxiDV6JpYyPK2YnollhJnNgnZLwB1W4yeV07BWNAVYq7ExqvqDXXcrMElVL/HL5G0AfzqsmqjCkfIwdjgj2VIQxXZnFGWVDsJEqyIaGAKIKh3XrqiKKO+dDsWbgswux9KhDBrlt4jygXBY3kTaUeQHJJcwLL2YTkas0e4ItEowDLgXuBrIUNUkEZkC9FTVZ+rp1wNLCr5CVbd7lZ8EHFbVtXV0DXpa02HVRqHLwU5nJFsLoshzRuF0hREuah+Um2+HgOF20+W7L62I8p+8XXc6lM49yZ1sOa/9/Ya1qPMKtMPyICjhDiXCoQxNLWZIWglp0Q1nTDa0fQLtsP6MFbLoYeBFVU0WkV7A26o6oklWtXMC7bBqUlIh7CyKrBJyHC4PJ1wUl1EiBgyprKTbN4sZMH8W/T97t850KPnd+5I7eQa5U6aT32tgs+dtKw7LGweKQ5SESDfD04oYlFpSlb/LEHwE2mHlAeNUdY+IHFLVVFs4cUhVU5pkVTunrTmsmpRXCruLI4wSsY1QlQ5l3ltWOhRn7cfD+/sMsoPyNj0dSlt0WN54xBoZMS5GpBfRP7mUKCPWCCoC7bAOAFm24s/jsKKAPFXt2CSr2jlt3WHVpEqJ6Ixky9Foo0QMIJ50KNnz36LPorn1pEMZYQXlbWQ6lLbusLzxiDV6JJQxPL2IXgllhJmPY5sn0BeHv8GK+PC8V9nlWJEgDO2AMAd0jnPROc7FuMyi6krEwmh2FRklYmtRMx1K76UfMWD+rFrSoawka91KKx3KkDGW8zrzknaVDsXzWdtcEM0OZyQK9E8qZVh6MV3iyo1YI8Tw1WHdDeSIyKVArIi8D4wCak07IiJfq+qJ9vP7VPUPLWKtodUQgYyYCjJiKhiZUWyUiAGiIjqG9adfzPrTL7bSoSyeS/b8WcenQ1mznM5rlnP63+9hx4iTrIjyZ1xMcWqrp53zG57P2drDMaw/Gk24KENSSxiSVkyHGCPWCAUacw+rA3AV0BPr8vArqlqrxElEjgApqqoiUuCPC8JtnWDbEmwK3krEbc4oCo0SsdWIKjxK35z/kT1/drV0KN64HQ62nXgquZOns+G0CylNSgWCa0uwITxijfgIN8PTixiUUkJCpBFrBJqAnWGJSDjwOHCXqpb6NqHMA2KBXOAa4OXa2nkuErdHQsFh1aTUViLmGSViqxJ9JJ/+n71H9vxZdFuRU2s6lMrwcPLGnEHulOmUZXdgY++zAmCpfwkTKzRoh2gXw9OLGZBcQnS4EWsEgkCLLvKBdPVxOSYiKcBNWGlIrsYKgXQcqnqtL+MFI6HosGriUSJuL4xkS0G0USK2ArH5+xjwyTtkz59Fl1VLa02HUhkRwQ9TfsTyq+7kYJ/BAbDS/3jEGt3iyxieXkzvxFLCzc51qxFoh/Ui8KGqzm785DJHVc9vbL9gxzis4zFKxNalKh3KvLfo/H3t+qhNJ01l+dV3seOEk/wWWSPQRDrcqELf5FKGpRXTLd6INfxNoB3Wf7DShSwB8rDScgC+b+uJSCbQFdhR19lXe8I4rIYxSsTW41g6lLfIWlczUxDsGjya5VffxcZJ57d6MN7Ww8rh5RAYnFrM0LRiMoxYwy8E2mG9VFddQ9t6dlqP/wBTPV2wIr9fraqHGpw8SDEOq/HUVCLucEZRapSILc4JOc/Rfe4C+i3833Fbhvnd+/LVlXfy/TlXUBnVtBTnwYCghIkSG+5meHoxg1JLSIo0ObxaiqALfntscnkZSAfuxMor1Rt4BCtKxjV+nTyAGIfVMjhdDnYYJWKL4lEJpmzbwOhXH2PI3FcJLy+r1saZlsmKy25h5bQbKEts38FsPGKNtGgXI9KKGZBSQowRazSLgDgsEYkAfgOMwLok/BdVbdQaWkR2A9neqUhsUcYP7TlKhnFY/sEoEZtPTVl73MG9jHzjSU6Y9e/jQkKVxcbz3cU/5esrbqMws0trm9rqRIgbN0KXuHJGpBfRJ8mINZpCoCJd/BUrosVi4P+AOODXTbChplc0FyUMTSI6XOmTVEafJGtF4HILu4qMErE5FKVnsfjWB1h23S8Z9u4LnPifx0ncvwuwsiaP/s9jjHzzSX44+zJLWdh7UIAt9h8uW/yzzWklSnUr9LUja3SLL8dhPlIBpaEVVh5wvqquFpERwJuq2ii3aSdvTALuwBJs9AAeBQpV9aqmmd32MSuswFCpsK84gm1GiVgnDV0cdrjKGfjxm4x55e902PzDcfWbTprKsmvuZueICe1WWVgdS6whAoNTihmSVkxmTEVovPUmEqgVVqqqrgZQ1ZUikt6E+W/HSi+/iWMrrfnAT5swlsFQL2ECneJcdKolJuKWwmh2GiVig7gjIvn+vKv4/pwf03vpx4yZ+Sjdvv28qr7Pkg/ps+RDdg0Zw/Kr72LDpPNbJVNy4BD7zBS+PRjH6kOxRIcpw9KKGJxaQnKUEWu0Fr7GEvTQ6L8pbCXgWSLSkWOy9j2NHcdgaAoNxUQ0SsR6cDjYfPJUNp88lU6rlzPmlUerKQs7r1nOxXfPCBllIYAiuNyCyw1f7kvgy30JpEZVMCK9iAEppcSGm9MOf9LQlmAllrLPQy9gi3cbVe3nH9OCG7MlGDx4lIh5hVHkFR5TIrZXIUdzYgmm5q1n9KuPMXjuq9WC7wI407OOKQsTklvC1KAh3OFGVegUW86IDkX0TSojwhG6SsNAqQSvbmhwVZ3ZJKvaOcZhBS/VlIiFURwua19KxJYIfht3YA+j3nySEbOerVVZuOqS61lx+a0hoSysSaQdFqp3YinD04vpnlAWcmKNoL2HFaoYh9V+cLmF3UURbLPPwQ6UWErEChXcQahEbMlo7ZHOAoa/+wKjXnuiSlnooTI8PCSUhXVjizWAgSnFDE0vISvGFRJijUAncDQYQpYIh9I9oZzuCeVMxFmlRNzujGRziCsRy+MT+erKO1hx6c8tZeHMR+mwJReAsIoKhrz/KkPef5VNJ09l2dWhpCwEb7HGqvw4vj8cS1SYMjS1iCFpJaQYsUaj8bvDEpEJqrrU3/MYDK2FtxJxrLcS0WndBQtFJWI1ZeGSjxg781G6rlxSVd/n8w/p83koKQur4y3WWLY/ga/2J5AUaYk1slNKiYswYg1faI0V1scish14DivpY0DjB4pIGPAwVp6uaCyJ/Y2qelBEJgAvAWnA06r6W69+vwIyVfWO1rfa0JappkTsUF2JuNXOzhwySkSHg80Tz2HzxHPo9N0yS1mYM6dWZeHyq+5i7TlXUBkZFWCjWxe3Cm4gvyyCnN2JLNydRMfYckakF9M3qZTIMHNMUxetEUswHitaxnXAMOA94DlV/cyvE9dtz2+wcnSdBeQDLwKxqnq2iKzGCkW1FPgGuERVvxWR/sA7wGhVLfJlHnOGZfCmLSkRWzvjsKUs/AeD5/7HKAvrIdLhplKFXomlDE8rpmdi8Io12oToQkQSgATvMlXd3Yj+g4GfAFcAR4HngedVNd9nI5qJiGwD/qiqL9ive2Ndau4J/ACkqGqZiLyJ5aRmYYWmuq8xTjYhIV5HjmxacrwjR0pITo5pUl9DcOBWoaxSKK10UFrpoMItCIq7FZxXdPlRSiOT/D5PTTq4yrhm/26uOLCbxMrq5zeFjjDe6NCRlzI6sy/EVly14bBjLMRGuIkLdxMVFlxbhs35Dlu0aHmz04uMA2ZiRVqvKgZUVX1OniMiXbBWWtdhxRPcBowCbmkNebyIJAFHgBGqusqr/ChwJVacxL9gOaivgXOAM4AhvuT9EpEbgBsAIiIiRg4cmN0kOysr3YSFtfOtI8NxKOBWy5n5c9/DoZW4JXA5r+IqK5lxKJ+rDh4gs6J6LG2XCHOTk3kxPYPN0e37EnJjEMAhVi6vYFh0Nec77LvvVjfbYa0GPsFaEVXbElPVbQ30DQcuwArFNAn4AOs8a76qqohMBN5V1bSG30rzEJGuwHagl6pu9SrfhrUVuBJ4DCsdyr+Bj+3HGOA2LOd1AMvB7q1vLrMlaGgO1ZSIBdHsKWo5JWJrbwnWhcNVzqCP3mT0K8eUhd5sOnkqy665h53Dx4eQsrBuwrDiGSZGVjA8rZiBqSXEt1GxRqBl7T2Bu7RpB167ASfwAnBtzS96VV0sIptr7dnyFNo/a+6HJAMFqroWONNTKCLzgbuBscCpwGlYW5qPYm1rGgx+oTYl4sHScLa3IyWiOyKSNedfxZpzbWXhy4/QddUxQbFHWbhz6FgrG/Ip54WUsrAmlQgoHCqLYPGeBBbtSSQzxsWI9CL6JZcSFQJiDV8d1nKgP7CuCXNcBcyrz9mp6ugmjNtoVPWIrVg8AVgFICK9gERgtXdbEbkOOKCqc0XkF8ByVa0UkcVYAX0NhlZDBDrEVNDBS4l4tDyM7bUqEYMsuaWXsrDzd1/aMQvnVFV3Wb2MLndNJ79HP5ZfeWdIKgtr4lll7y6O5MDOcD7ekUyPhDJGpBfRM7GMsCD6528MvjqsT4E5IvIMUHOF9HoDfW9X1Y9rForIB6p6jo/ztyTPAr8UkYVYKsG/YDnUPC/bOgK/BCbYRZuBu0UkBpiKJdIwGAKGCCRHVZIcVcLQtBLAUiLudEaytQ0oEZvKrmHjeOfR2aRuXcfo/zxWTVmYlreBqX+6iYlP38/Xl9/KqkuuD3llIRxbYW8uiGaHMxIFBiSXMiytmM5x5e1qN9XXM6ytdVSpqvZqoG+BqibWUp7fGudWtcwbhuWkrgGigAXADap60KvNe8Abqvpfrz7PAxdhBf+9XFXrXW2aMyxDoPHERNxWGMUWOyZihCgdd3/RJs6wfCH+wG5GvvEkI2Y/S7SzoFpdWVwCqy6+nq+vuBVnRucAWdg2EZRwhxLhUIakFjMkrYT06EYli28WbULW3rhJ5XL76fNY5z7efr4vcKWq9vHL5G0A47AMbQ1PTMQ1y35gY8ZJVChUBkkoqUhnAcPfeZ4TX3uChAPVb9JUhkewdurlLL/qDvJ7DQyQhW0XB4pDlPgIN8PTixiUWkKCn8UabcJhiUgmVk6r7aq6v4G2nlVZNyxlngc31rbiA6r6kc+TBxnGYRnaKjk56xl78gCW7Elg1cFYKjV4tgwdrnIGffgGY155lPStx29ybJx4Dss9MQsNxxEubhQhI8bF8LQi+qeUEu0HsUZAVYIikgK8inV+A6Ai8iFwdV2hllS1p913jqqe33izDQaDv4gOU87oUsCoDkXM35HEdmdkUATudUdEsuaCq1lz3pX0+fxDxsx8hK6rvqiq77v4A/ou/oCdw8ZZysKJ54a0srAmnn/jPcWRHCwNZ/7OZLrHlzE8vZheiaWEt/Ffla/m/cP+OQCIALKx7jn+vaGOxlkZDG2X5KhKZvQ5xI/6HCI1ykWEo23e6zkOh4NNp5zLay/m8OqLOWyYdF616i7ffckld07j+mlDGfreS4SVlwXI0LaLy+2gUoUthdHM3ZbM42uyeD8vme3OSNpq1ilfVYKTgWxV9WRq22And/yhtsYi8oSq3mY/f7auQX2JHmEwGPxP1/hyrs8+wJpDMXy2K4kKd/CkStk1fDzvDB9P6tZ1jLFjFoZVuABbWfjHGzn5qftZUaUsbP2wVG0dT1DmtYdj2HA0mnBRBqeWMDStmA4xrSfWaIjGfCJr+tz6/hSLqPG8rofBYGgjiMDQtBJuHrSPUR2KCBd3VUy7YOBQzwF89Pt/8/QHG1l29V2Uxh8TJycc3MOpT/yam6f2YtJjvyK+RrJJgwfB5XZQUhnGNwfimLk+nafXZvDF3ngKygP/B4yvsvZXsKJD3AHkAT2woj0UqupVfrQvaDGiC0NbxdfPV0G5gwU7k9haEBU0qy1vogqPMsyjLDy4p1rdMWXhneT3alrMz1AiTNyAkB7tYnh6MdnJJUSH1+07/CW68PVTeDvWnaVNgAvYiJVLyuSGMhjaKYmRbi7pdZjL++bTITqIzrdsyhKS+Orqu3hm7gY+uO85DvY49gUaVuFi6JyZXD9tGJfccTGdvYQbhuOpVOu8a19JJJ/tTOSf32fx5qZU1h2JpqIVPxY+nWHZSsCz7AgQXYEdqrqnrvYispHjtxBrG7efr4YaDIbA0CnOxXUDDpB7OJoFO5NwuSWoVlyVkVFeysIPGDPz0erKwkVz6btorlEW+ojL/rfPK4xmd1EkboW+SaUMTy+mW7x/I2s0KuOw7aTqdFRePNA0cwwGQ1tEBAamltI3uZTl++JZti/ezpwbHPe3AFtZeB6bTjmPzqu+YOzMR+i7aG5VdZfvvqTLndM42KM/X111F2unXhbyMQsbwiPWyD0Sw6aCaMIEBqUU41D/fC4a5bB8pTVyWxkMhtYnwgEndXQyPL2Yz3YlsuFINBUaZMF2sZSFbw9/h7QtuVY25A9eq1IWpuetZ+ofb+Dkp+4zykKfEVxuwQV8ezCO/i7/5Fvzy7pXRLK8nneq6+GPuQ0Gg/+Jj3Bzfo8jXNnvIFkxwXe+5SG/VzYf3fdsvcrCn53Tm0mP30v8AZ+Tq4c0ivjtHpe/Nmo3eD3fCeyo8fCUGQyGICYztoKr+x/k3G5HiAuvJDxIHZezQydy/u8hnv5gMwtv+zOF6R2r6qKdBYyd+Sg/O6cvZ//hBtJqSTZpaB18clgi8ps6yu+to8sgr+c9gV41Hp4yg8EQ5IhA/5RSbh60j5OyColwuAmT4Lm/5U1ZQhLLr7mbZ+Zu4MPfP3ucsnDY/14+piz87ssAWhqa+LrC+mUd5ffUVqiqO7yeb6vr0VhjDQZD2yXMAWMzi/jZoP0MSikmXNz4IBZuk1RGRrH6wmt4fvZ3vP332ewcOrZafd9Fc7ny2lO44rpJ9Fn0PriDc2UZbNQruvA6Z3LYkvaaKUJ8CtAlIj8CrgW6YG0HvqyqbzbeXIPB0NaJDXcztftRRmcU8fGOJPaVRFQlGQw6HA42TjqfjZPOr1VZ2HXVF3Rd9YVRFrYSDX2KPGdNMVQ/i9oJfAI83tAEIvJL4Anga6wgul8Dj9nlBoOhnZIeU8GP++VzUc/DJEZUBK0ww8Ou4eN5+x/v8Nzs71h9/tVUhh+LLudRFt50Xn9Gz3yUqMKj9YxkaCoNOayeQG+gkOpnUN2BBFV92Ic5fg6craq/U9UXVPV3wNnALU0322AwBAu9Esu4adB+TulYQKTDbYf5CV7ye2Xz4f3P8fTcDSy76k7K4hKq6hIO7Oa0x+81ykI/Ua/Dss+a8lQ1ucb50w5VLfVxjlhgdY2yNVirNoPBEAI4BEZlFHPzoH0MSysmXBQJ0vMtD86MzuTc/jBPfbilTmXhTef24+w/3khqLckmDY3HV5XgCyJySo2yU0TkOR+6v8LxMQdvB8zlYoMhxIgOVyZ3LeC6AfvpnlBGRJCvtqCmsvDf5Pc4FnEu3FXOsPde4oZLhnLxnZcYZWEz8fUk9DxgWY2y5UCtyRlFZIGIzBeR+cBQ4EERyRORRSKSBzwIDGuizQaDIchJja7k0j6HmNb7EMmRwX++BR5l4bU8N3t1rcrCfjnveykL5xplYRPwNTRTGMfnv6oEIutov6TG688bY5TBYAgNuieUc8PA/azOj2Xh7kQqgyhxZJ14KQu7rFzKmJmP0HfxB1XVlrLwYg72HMDyq+7ih7MvNcpCH/HVYa0FLgVe9SqbQR0Zh1X1D820y2AwhAgOwcqxlFLC0j0JfHswDrcSXIF162DniAnsHDGBtC0/MOaVfzDow9ePxSzcuo5z/nA9E5+6j6/tmIXlXqGhDMfj658yvwOeFZH/isifRORN4Dmg1ggYNRGRMBEZICIni8hEz6OpRhsMhvZHVJhyWpcCfpq9n16JZfbF4/ZBfq+BVcrC5VfWriy8eWovTnni18Qd8CUhRmjik8NS1UXAGOAgcAKQD4xV1ZyG+orICcBmrNVYjv1YiHWPy2AwGKqRHFXJtN6HuLRPPmlBmDiyPpwZnVl4x8M89eFmFt72IM70qjjhRDsLGPfyI/zs3L5GWVgHPm8Wq+pqVf25qp5j/6wpVa+Lx4B3gSSgAEgE/g1c01hjm4uIqIgUi4jT65HkVf+AiOSLyAYRmeBVHiEi34rIyNa22WAIVbrEu/jpgANM6XKU6DB3u1pxlSUks/yae3j6/Q18+LtnyO/et6rOoyy8ftowLr5rGp2+q6l3C118dlgicpKIPCsi79uvR/q4rTcE+JWqFgKiqk7gF8Afm2Rx85msqvFej6NQtRK8FOui9K+Ap736/BqYr6rftL65BkPoIgKD00r4+eB9jM4oIlzcOIL8/pY3lVHRrL7oOp57ew1vPzqLXUPGVNWJKv0WzuGqaydyxXWnGmUhvt/DuhyYA5QCHiel+OZ0XF7Pj4pIhl2WVUf7QNEH+FpVjwAf2q8RkcHANOD+wJlmMIQ2EQ5lYqdCbhy4n37JpUEdWLdWHA42nnoBr768mP+8sJBNJ0+tVt111VKm3XExP5kxgiFzXsHhKg+QoYFF1IdMWyKyFrhaVVeIyGFVTRGRSGCXqnZooO9HwL9Uda6IzMRyVMVApqqOb4H34DMiosBeIALrXO0vqvqOXTcQyymPBk7FikQ/AVgK3K2qNaX6tY1/A3ADQGZmxsg333y1gR6143SWEh8f3aS+BkNDtIfPl8stFJSHUaHBHi+jbhK3bWXA22/RPedTwioqqtUVp6Wz/sJL2Hz2uVTExgXIwrqJdjlJTmyaVP/UU6d8o6qjaqvz1WEdVtUU+/khVU0VEQdwo3kRGwAAFepJREFUUFVTG+jbGXCo6g4RSQMeBhKA+1R1fWPfTHMQkdOxHBDABcDLwEWq+rFdfxNwI5a45P+AqVhxEx/CCuCbAcxT1QcbmmvUqKG6YsX7TbIzJ2c9kyb1b7ihwdAE2svnSxXWHYlmwc4kyisl+O9v1UHCvp2Mev2fDH/7OaKKndXqSuMTWTntBlZcditFHTrWMULr03//51w0pXeT+or0qNNh+fovvFlEaq6GxgMNOhxV3eXJj6Wq+ap6vape6m9nJSJXeIsr7Pk/VdVS+/Ff4D/AFV62PqOqI1T1TKAcuA64F3gU+ABr5TVFRM70p+0Gg6FhRCDbThw5PstpJY5sh+utwswuLLzjLzz10RZybn0AZ1pmVZ23svCsP91Eal6rrgFaHV8d1gPA/0Tkt0CEiNwFvIGPwgkRGSsiz4jIXPvnuCba6zOq+pq3uKKOZm44/naiiAjwPHC7LRIZBnyhqpXAF8AIf9lt+P/27j3O6rrO4/jrPTcGBmYAAUPkflMg0yRpSxOxm67mLSsVQkXNdWvVtc223Nptt9rS0q6WYmZmdlFXSzMxLwmmsKnrJU0TMxGFGQYYYLjPfPaP32/geJobw5yZc868n4/HeZz5/b6/y/cMP+Zzvr/v9/f9mO2ZshJ4x5s2cf60Wg4YsoUyBUXVv5XaNmgwj571Ka6+88+tjiw8+H9+wLmnHFTUIws7+xzW7cDpJM9i/RWYA5wdEXd3tK+k+cD9QBXwRPr+23R9j5E0Q9JhkirSYeonAvOAn7ey+fnASxGxKF1eDhwjqT9JK+vFnqm1mXVWVXkzx49bz0en1rHfgOJ6fitTy8jChbc8xW1X/JyVMw7bVfaGkYUL5jDxobuKamRhm1MzSfp6RPxz+vOciLgXuLcL57gM+EBE7HpQWNL1JDNl9OSM7cOBbwPjSG73LScJur/M3EjSaJLZ5GdlrL4UuIlkpOCtwO25r66ZdcWI/juZN2UNLzb0455XB7Ntp9hRhP1bUVrKC3NO5IWjTmD/Jx7m7TdcwaTFv95VPvqJJYx+Ygl1Ew5MsiEf8xGay9ua/rUwtDnoQlJDRNSkP2+IiC5NciWpARgSEc0Z60qAdS3HL0YedGH5qi9dX03N8NiaKha/PojmEE1R+PMTtmfYi88w68YrmXb3zX8zsnDDiFH84Yx/4v9OWpDzOQt7Y9DFy5K+nD6DVS7pNEmnZ786cf47gA9nrTsVt1LMLMdKS+CwEY1cMH01M4YWR+LI9qyZNIO7/uM6vvfL51k672K2DdjdfV9du5I5V17KBcdO5MhvfbYg5yxsr4U1kyRv1QRgPLCilc0iIia0su81GYsDSALUo8DLJLfk3g78PCLm7UXd85pbWJav+vL1Vb+1jN+sqGFVY3lR3ibM1m/jeg75xfeZefO3GVi/+g1lO8sreOa4uSybdzFrx3Xv9dDjLayI+ENEvC8iJgMbI2J8K6+/CVap8ozXDuAnwEsko/JeSpd3trGvmVlO7FO5kzMm13PyhHXUFEniyPZsGzSYR8++lKvv/DN3X3Y1a8dM2lWWObLwpEtOZb+nlvZiTTunvUEXtRExIl38nz05aESctVe1MjPLofHV2/jYtFqeWDOA371WTVNAUxG3uJr6VfLkyQt46oQzmfy7XzHrh1cw6pllQDKycOoDdzD1gTtYccjhPDr/EpYffgyU5N/vo70alUlqmVvjg3tzEkkDJX1Y0iclfUhSW89FmZn1iBLBocM3c8GM1Rw8bDNlai7q/i3YPbLwxhsWc9O19/Hi4VlzFj6xhFMvOokFH34rM36Vf3MWtpdx+F7gWUnLgf6SFrW2UUS8t70TSJqeHquJ3X1YV0l6b0Q805VKm5l1l8rS4D37b2Dm8EbuXVHDK5sqinaap10kVhx6BCsOPSIZWfijrzPtNz/dNbJw+PJnOe7z5/Cu73w+GVl48jlsz0g62Vva+1eZS/IM1UMkfU8Pt/HqyFUk+a/GRMQRwBiS1B3f6Hq1zcy615B+TXxo0lo+NHEtQ/sVf/9WizWTZnDXF37A9375PMvmXtT6yMJjJvCub11G1ZpVvVjTzk9++62I+ESXTiCtAUZGxI6MdeXAqojYpyvHLAQeJWj5ytdXxyLg6fr+3PdaDU3NFH+LK0O/Des45JZr2hlZOI+lH72YdWOntHmMXpn8Nr2dR1vBStKxra3P0kByGzDTOJLsw2ZmeUeCg4Zt4R+nr+bQ4Y1F//xWpm3VQ3aPLPzsd1sZWXgd5538Zk665FRGPr2sR+vW0deGRzIXJK3NKv9pJ85xA3CXpLMlHSXpbOBXJKk9zMzyVkVpcNSojZw7rZZJNS2JI/uGpn6VPHnKOVx769PcdvnPeG3G23aVtYwsnD//cE4/52gmLv51j8xZ2N6gC/jbmcw7Wm7NF0mexboUGE3yAPIPgcs7sa+ZWa+rqWjilAnreK2xnF+/MpiG7aXsaO4btwmjtJQXjj6JF+acyOjHFzPrhq8xacnuec/HPL6YMY8vpm7iNJbOv4Rn35c9sVH36eg3nt0G7mj5DSSVAocCX4uIqRExIH3/ckT4wWEzKyj7Ve1gwQF1vH/0evqXNvWpFlcysvBd3PLNO7juZ4/x9HFzaSrb3eYZvvxZjvvcAs7/wAFMuvUXsHFTOwfrmpx+RUjzRz1A0sIyMyt4EkwfupULZqxm1r6bKFMzJX2kf6tF3eQ3c9cXfsD37/gTy864kO39q3aVVa9+lTdf+z245uZuP29HtwQrJH0mY7kya7m8E+d4liTN/Mt7WDczs7xVXgJHjNzEIcM2c9+r1fy5oZKdITrXU1IcNowcw/2XXM7D536Gt6ZzFlatrWXHgCrKz+n+W4MdBaxHgcx08EuzljuT1vJG4HZJl5Mkf9zVho6I33eynmZmeWlgeTMnjF/Pqs1l/OaVwdRvK+sz/VsttlUP4ZEFn2bZ3IuYceePGVX/DAfVdH8Kk3YDVkTM7oZzXJW+35h9eKC0G45vZtbr3jRgJ/OnruGF9ZUserWG7c3qc4GrZWTh1trFHJSD43fUwtprEX3oiTsz69MkmDpkKxNrtvK/tVX8fnXfSBzZU3IaTCRNknSSpPG5PI+ZWT4pK4G/e1Mj/zCtlmlDtlCmoINB1dYJOQtYkk4GngNuBZ7r5KwYZmZFY0B5M38/dj3zp9Yxqmp7n5mfMFdy2cK6DPgMMAj4fPqzmVmfM7z/TuZOrufEcesYVN53JtbtbrkMWONJHhhuBL4OTOpgezOzoiXBxJptnD+9liNHbqCipJnSvvTgcTfIZcAqjYhmgHSm9oocnsvMrCCUCmaO2MwF01dz0NDNfWpi3b2Vy1GCHT10TER8KYfnNzPLW5VlwfvGbOBtIxq5Z0UNKxv7QOLIvZTL307LQ8ctr6VZy+/u7hNKWijpj5J2SlrYSnmppMsl1UnaKOlWScMyyt8p6QVJ9ZL+K2vfT0u6srvrbGZ929DKJk6bvJYPTlzL4Ar3b7UnZy2sbnroeE89BfwC+Fgb5Z8GTgBmAfXAD0geaD4mLb8auIQkk/Jjkm6LiMclTQXmAYflsO5m1oeNG7Sd86bV8mT9AB5cWU1T9K3EkZ1RVL+NiPhmRNxD28khzwO+EhEvRUQD8Cng/ZLGpeWTgEURsZakRThJkoCFwCfSASRmZjlRIjhk2GYumLGaQ4a5fytbUQWs9kiqAcYAj7Wsi4jlJMGtZRaRp4BjJe1D0pp6Gvg48FxE3N+zNTazvqpfaXD0/hs458BaJlRv61tpTNqR86mZ8kjLTIwNWevXZ5QtIJn78HPAV4EtwD8CsyT9G0m/Wx3w8YhYlX0CSeeRtOLYd98RPPjg812q6KZNW7u8r1lHfH0VluHA4GbRsL2UpiiM9pZ2NObkGivYgCXpDOD7LcsRMbCDXTam7zVZ6weT3kKMiD+SMRu9pEXAJ4G3A0cBc0iC2teAM7JPEBHXANcAzJx5UMyePbXzHyjDgw8+T1f3NeuIr6/CFAHPrO3PfStr2Nmc3/1bU2sXM3v2xG4/bv5+4g5ExE0RMbDl1Ynt1wOvAG9tWSdpAknr6qns7SWdDdRFxJ3AW4ClaULKh4BDuuljmJl1igRv3mcLF0xfzdtGNPbJxJEFG7BaI6lCUiVJ2pJSSZWSMh9Yvga4VNJ4SdXAV4B7IuLlrOOMBC4FLkxXLQdmS+oPHAu8mOOPYmbWqorS4Mj9NnLetFom12ztU/1bBXtLsA2LgCMzls8EfgfMTpf/GxgC/C/QD7gXmNvKca4GPhcRa9Ll24HjgNeBl4DTu7neZmZ7pLqimZMmrOP1xnLuXjGYddtKiz7/VlEFrI6e/Upv6X0yfbW33Ymt7HdW+jIzyxsjq3Zw1tQ6nltXyW9X1rC9SXndv7U3iipgmZn1RRJMG7qVKYO3snT1QB6pHUhzs2imuBJHFmcYNjPrg8pK4J0jN/EP02o5sAgTRzpgmZkVmaryZo4ft555U+oYOWBH0cxP6IBlZlak9h2wk49OWcPxY9cxsLyp4AOX+7DMzIqYBFMGb2Ni9Wr+UFfFklWDaA7RFIXXv+UWlplZH1BaArP2beSC6auZUaCJIx2wzMz6kP5lwTFjGjjzgDr2r9peULcJHbDMzPqgYZU7OWNKPSeNX0d1eWEkjnTAMjPrwyZUb+P86bXM3m8DFSXNlObxVE8edGFm1seVCA4dvpnpQ7aw+PVBPFlfRVNA5NmDx25hmZkZAJVlwXtGb2DBgbWMHZR/iSMdsMzM7A2G9GviI5PWcurEtQzplz/9Ww5YZmbWqrGDtnPugbW8e1QD/Uqbe73F5T4sMzNrU4ngLcO2cOCQrTy8aiCP1Q3stf4tt7DMzKxDFaXBUaM2cu60Wib1UuJIBywzM+u0moomTpmwjtMm1zOssmcn1nXAMjOzPTaqagcLDqjj/aMb6F/a1CMtLvdhmZlZl0gwfegWpgzeyqOrq1i6emBOJ9V1wDIzs71SXhIcMXITBw/bzP2vVlOyJjeT6vqWoJmZdYtB5c2cMH49g8qbcnJ8BywzMysIDlhmZlYQHLDMzKwgOGCZmVlBcMAyM7OC4IBlZmYFwQHLzMwKggOWmZkVBEXk5onkvk5SHfDXLu4+DFjTjdUxy+Try3Jtb66xsRExvLUCB6w8JOkPETGzt+thxcnXl+Varq4x3xI0M7OC4IBlZmYFwQErP13T2xWwoubry3ItJ9eY+7DMzKwguIVlZmYFwQHLzFolaaGkH/Z2Paz4SLpM0oN7up8DVi+Q9BFJiyVtkLRzD/b7oaSFuaybFSZJX5H0x/Saek3StZKG9na9rPhI+qKkv6TXWq2kWySN6YlzO2D1jnXAd4GLersiVjSagLnAPsBbgP2B63u1RlasbgQOjohqYBzwCvDT1jaUVN6dJ3bA6gURcU9E3Ay8lF0m6RBJSyQ1SFor6feShkj6FHAGMF/SpvRV2uOVt7wUEZ+JiCciYkdE1AHfBma3lKet8xvTltd6SSslfSzzGJLOlrQ8/eZ8I1DZs5/CCkFE/CkiGtJFAc3AVNh1nd0k6XpJa4Fvpuv/XtKz6d+tO0lmwthjDlj55zvAImAosC/wz8D2iPgqcBNwQ0QMTF9NvVhPy29HA09lrfsg8CuSa+sTwLcljQWQdATJtXd+Wn4v8OEeq60VFEmnS2oANgEXAv+eUXwq8BtgOHCJpAnAbcCXgMEkQezcrpzXASv/bAfGAKPTb8uPRkRjb1fKCoekU0j+IFyYVXR/RPwyIpoj4jZgPXBwWvZR4JaIuDcidkbEj4BlPVdrKyQR8ZOIqAFGkgSrpzOKl0TEzyKiKSI2A6cByyLix+m1tQi4vSvndcDKP2eR/LssSTs2/1NSWW9XygqDpFOBa4EPRMTjWcWvZy03AoPSn/cHXs4q/0u3V9CKSkSsIrne7swY5PNy1mbddm05YOWZiPhLRJwdEfsDHwDOIfn2C8m9YrNWSToL+D5wfEQ8sIe7ryTpQM80vjvqZUWvDKgC9kuXs/9Oddu15YDVCySVSqoEKtLlyvQlSfMltfzDrwd2pi+AVcAESf53szeQ9E/AFcD7IuLhLhziR8AHJR0tqUzSXOCwbq2kFTxJJZI+LmlEurw/Sd/ny8Cf2tjtZmCWpNPSa+vdwAldOb//8PWOecAW4B6gNP15CzAWmAM8JmkT8AjwE5LBFgALSb7J1KcjvTxK0Fp8A6gGHsgYRbqpsztHxEMkAzEWAmuB9wM/y0lNrdAdCzwjqRFYCmwG3h0RrT5TGhHLSQb8fI7kS/jFJNfZHvNcgmZmVhDcwjIzs4LggGVmZgXBAcvMzAqCA5aZmRUEBywzMysIDlhmZlYQHLDMeomku9NZ+DuzbUg6PNd16kQ9HpR0WW/Xw/omz1FnlgNZD+32S9+3taxIZ9s/pofqMhv4bUR0+P89nYXgUySzXAh4FbgV+FpGSgmzXuEWllkOZKSAGQjcANyUtS7vSDqTJP3IImBqOhv3cSQT5B7Ui1UzAxywzHpN9u01SeMk/ULS6+nUWw9L2qeV/YaniT0XtszkL+lESY+l+z0n6Yx0/X7A3UBpxpRN81s55kDgKuDLEXFFRKyGXZMxXxwRi9v4DNdLWiFpY5qg7/SMsiHp56lPE5I+k+bdajNR6d78Pq34+ZagWR6QNAC4nyS4HECS+uNtJPnRMrebAtxFksjzv9J17wGuA04EHgZmAvdIWhERD0k6huSWYHstu3cANSRzV+6JJcAnSeaIOxX4kaT/i4hngX8BBpDMkdkITAZ2pPt9hyTJ35EkX5wPzf6sZtncwjLLD8cB/YELI6IhTXT3SERszNjmCOB3wL+3BKvUhcA3ImJxmpxxGfBjdqel6Yzh6fvKPal0RFwXEfVpsr6fkmQ5np0Wbwf2IUmfroh4ISL+klHmRKW2RxywzPLDOOCltma8Tl0I/JG/nUV9PHBpejtwvaT1wJnszk/UGXXp+6jO7pCmmviCpOfTW3vrgbewO/hdDtxH0odXJ+kGSfumZU5UanvMAcssP7wMjO8gZcyZJP9nb5XUL2P9X0laXYMzXoMi4ti0vDOJP38PNJCkM++s00gSjJ4CDImIwcCTJKMLiYjGiPhsRMwAppMEw8vTsvYSlZq1ygHLLD/cRXKb7EpJNWmSz7dLGpSxzSaSXERlwF2SqtL1VwEXSToi3a9C0qGSZqblq0gGXbSZ5TUiNpHkKfpXSRdLGg4gaaykK1oGS2SpJkkuWgeUSDqbpIVFuu/xkg5Mg/AmYGu6PR0kKjVrlQOWWR5I+2/mAKOBPwP1JK2R8qzttpIMrqgH7pU0OCIWAeel268BXgeuBAam+7wAfBdYlt4ynNdGHa4nyQR7LPCipAbg1yQDJp5sZZcbSBL4vUjS9zUNyBxNOJFkmPwGkhbkFuDTaVl7iUrNWuUEjmZmVhDcwjIzs4LggGVmZgXBAcvMzAqCA5aZmRUEBywzMysIDlhmZlYQHLDMzKwgOGCZmVlBcMAyM7OC8P83wNH0O0EwvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp = partial_deltaprob('Pclass', X_all, shap_df, shap_sum, probas_cat, func_shap2probas=intp)\n",
    "\n",
    "plt.axhline(0,color='black',zorder=3)\n",
    "plt.plot([1,2,3], dp['mean'], color = 'red', linewidth = 3, label = 'Avg effect',zorder=4)\n",
    "plt.fill_between([1,2,3], dp['mean'] + dp['std'],\n",
    "                        dp['mean'] - dp['std'],\n",
    "                        color = 'lightskyblue', label = 'Avg effect +- StDev',zorder=1)\n",
    "yticks = list(np.arange(-.1,.16,.05))\n",
    "plt.yticks(yticks, [('+' if y > 0 else '') + '{0:.0%}'.format(y) for y in yticks], fontsize=13)\n",
    "plt.xticks([1,2,3], ['1st','2nd','3rd'], fontsize=13)\n",
    "plt.ylabel('Effect on Predicted\\nProbability of Survival',fontsize=13)\n",
    "plt.xlabel('Ticket Class', fontsize=13)\n",
    "plt.title('Marginal effect of\\nTicket Class', fontsize=15)\n",
    "plt.gca().set_facecolor('lightyellow')\n",
    "plt.grid(zorder=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp = partial_deltaprob('Fare', X_all, shap_df, shap_sum, probas_cat, func_shap2probas=intp, \n",
    "                               cutoffs = [0,25,50,75,100,X_train['Fare'].max()])\n",
    "\n",
    "plt.axhline(0,color='black',zorder=3)\n",
    "plt.plot(range(len(dp)), dp['mean'], color = 'red', linewidth = 3, label = 'Avg effect',zorder=4)\n",
    "plt.fill_between(range(len(dp)), dp['mean'] + dp['std'], dp['mean'] - dp['std'],\n",
    "                 color = 'lightskyblue', label = 'Avg effect +- StDev',zorder=1)\n",
    "yticks = [-.05,0,.05,.1,.15]\n",
    "plt.yticks(yticks, [('+' if y > 0 else '') + '{0:.0%}'.format(y) for y in yticks], fontsize=13)\n",
    "plt.xticks(range(len(dp)), ['(%s,%s]' %(int(x.left),int(x.right)) for x in dp.index.to_list()], fontsize=13,rotation=30)\n",
    "plt.ylabel('Effect on\\nPredicted Probability of Survival',fontsize=13)\n",
    "plt.xlabel('Passenger Fare', fontsize=13)\n",
    "plt.title('Marginal Effect of\\nPassenger Fare', fontsize=15)\n",
    "plt.gca().set_facecolor('lightyellow')\n",
    "plt.grid(zorder=2)\n",
    "\n",
    "plt.savefig('fare.png',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dp = partial_deltaprob('Age', X_all, shap_df, shap_sum, probas_cat, func_shap2probas=intp, cutoffs = list(range(0,75,10)))\n",
    "\n",
    "plt.axhline(0,color='black',zorder=3)\n",
    "plt.plot(list(range(7)), dp['mean'], color = 'red', linewidth = 3, label = 'Avg effect',zorder=4)\n",
    "plt.fill_between(list(range(7)), dp['mean'] + dp['std'],\n",
    "                        dp['mean'] - dp['std'],\n",
    "                        color = 'lightskyblue', label = 'Avg effect +- StDev',zorder=1)\n",
    "yticks = list(np.arange(-.2,.21,.1))\n",
    "plt.yticks(yticks, [('+' if y > 0 else '') + '{0:.0%}'.format(y) for y in yticks], fontsize=13)\n",
    "plt.xticks(list(range(7)), ['(%s,%s]' %(int(x.left),int(x.right)) for x in dp.index], fontsize=13,rotation=30)\n",
    "plt.ylabel('Effect on\\nPredicted Probability of Survival',fontsize=13)\n",
    "plt.xlabel('Passenger Age', fontsize=13)\n",
    "plt.title('Marginal Effect of\\nPassenger Age', fontsize=15)\n",
    "plt.gca().set_facecolor('lightyellow')\n",
    "plt.grid(zorder=2)\n",
    "\n",
    "plt.savefig('age.png',bbox_inches='tight',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interaction plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.DataFrame(shap_df[['Pclass','Fare']].sum(axis=1))\n",
    "temp.columns = ['deltaprob']\n",
    "temp = probas_cat - (shap_sum - temp['deltaprob']).apply(intp)\n",
    "temp = pd.concat([X_all[['Pclass','Fare']],temp],axis=1)\n",
    "temp['Fare'] = pd.cut(temp['Fare'],[0,25,50,75,100,513])\n",
    "bi = temp.groupby(['Pclass','Fare']).describe()[0].iloc[:,:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dInIwsFMzW2T26DNW1fXAT0TkXDJMRXt/VL6LMW55H2DFrhOs2Fn6hKrWi8grwJVikmy6GMu/ekwSUaDNtPsfGEMVBb4BNAEve6OFf2MsMldgBPQizM37Xe8U38c4sruYG3EDxlrxVOAyVV2RZX8dEbkeuF6M2f7zGKFLrn243vPjXp8eF5GfY4w4KoFDMMYol2R9kbLnceBGEbkEM6X7cdqnFrvEe18XY6xgq7y+xzHrq6cDn1TV9Jusn/sx1o4LMIZA25M7ROR8zBT2vzHGOPsBn8ZYSSbrLAaiqvrR7N5qdqjqMhG5C/i1iIzHTPGOBE5X1Q4JYlX1YhEpx4xCT1TVl1X1HTFuLn8TkeswrjMlwAHAdFU9zzv8PWCuiJyMGQ2uUdXdGdpQEfkCxlDrFRG5EfM9rQROwSTYPQwzM5DO48A3ReRVzGj7y6QlkhWROzDrzC9hfkcfwVjkPuXtf8jb9wbG+vlz3qF+a1pLOgNtIWMfhf3As8bsps5MzA+xCViPEaaU4zDreUsxIlWHuVEc6+0rwqxTLMdYpe3EZKJ+f1o7R2Is0fZ6bS3DuDSM8PafgxHS8rTjaoFf+LYFMy20w+vPn4FvesdW+eoVYdZHkpE9tnrtn9rZubu5TouBv3exP4SZHtzuvce/Ydw1FGN0kayjeNalGc5xKkYQmrxzvAH8CAhk0b8XvXN/La38aMz08RbMqGQtxjw/4qvzHPBED75XWVljemVB4AqMOMQwo/Pb09q+z7cdwLhg7Ep+h7yy73rfmaj32S8GvpT2PX4SIzDq39fJe5gA/Na7HjGMQP4L+JSvToo1JlCBMUjZ49W/DTPL4LdAPRczq7Hb+xyXAOf4zvEDjGDXe9/fF4GPD/S9otAfNlO5xULbv+mTVHWfge6LxWLpf+w0pmXYISIHYiwO/4OZtjyFdkdei8UyBLEjO8uwQ0SmYVwjDsH4k63DOMH/Uu0PwmIZklixs1gsFsuQxzqVWywWi2XIY9fsckR19SitqZncfcUMNDVFKSsr6r6iBbDXq6fY69Vz7DXrGX25Xq+9tnSnqqYHeegzVuxyRE3NZF599eFeHbt48XLmzNmvn3s0dLHXq2fY69Vz7DXrGX25XiI16/q5O8AQm8YUk5dsg5gcZVu87ZFpdb4nIpvE5OV6QkSm+/bNFpMDa4+I3OkPeSUm39mD+Xw/FovFYukfBp3YiUiNiHRmVXMDxjGzEhMFvhT4ne/YszAhkU7DxEJchom/mIz2fh1wNzARE13hU95x1Rjn4m/2+xuyWCwWS84ZdGLXFar6tqZGR3cxoY2SzAduVdXX1QSVvRQTTukYb/9M4DE1gVYXe9sAvwEWqJfSxmKxWCyDiyG3ZiciPwQuA8qBFlKjsh+MCccEgKo2ishKr/xpTN6uU0RkIyai+VUichpQrVkE1xWR+RhBZdy4sSxevLxX76GxsbXXxw5H7PXqGfZ69Rx7zXpGIV6vnIidiNyWTT1Vnd/fbavqAkw08mmYGHOrfLsrMPHk/NTRHrD4Ykysu68CDwAvYAKuftQTsrMwGbW/q6odPklVvQ0T647DDz9Ie7tAaxfDe4a9Xj3DXq+eY69ZzyjE65WrkV24P08mIjcDyQjnAa/Mn6BzgSdybajqWhF5GFgoIlPVpI5pAEaknb4KEzAXVd2IiRCfbPd24CZMhPQLMRmxj8VE3zi6f96dxWKxWHJNTsROVb/az+c7H5OgEBGpAdaqalUWh4aASZiQUA2Y6OGHYTJm46UCmeWVp+Blyd4PMy15BrBEVVtE5Bnyk8fLYrFYLP3EkDFQEZGxIvJlL5cXXgbf64DnVLXBq3YbcJ6IHCoiJcC1mPQcz6WdqwxjlPJ1L1biauBIz41hHqlTo5YCwHFt2DuLxdI5OTdQEZExwI2YBIQpXvGqGsx4UO9QTD6zG0WkCJMT7VHgKl97fxaRScAjmOnLF4BPqKqTdq6fAPeqlxBUVV8TkfsxIrcLs6ZnKRDijrJscysAgQCEAkIwAMGAEBQhGISgSPu+TGUBGeB3YbFYckk+rDF/g0ly+DXgf4EvYDJZ/7U3J1PVWkzyzfTyHcAJWRx/HWbE11WdCzOUXQLkIju1pR9xXYi1jfJ6NtqzQmmxDF3yIXYnYLIFbxcRV1UfEZGlwN8xlo8WS0FghdJiGbrkQ+zCwA7vdYuIlKnqehGZnYe2LZa80BehbBNIK5QWS87Ih9itwFhAvoaxerxUROqBbXlo22IpeBzXb2CTe6G0WIYj+RC7S4Ei3+v7MM7d/e5QbrEMN3ojlC1xl7c3tRAQCAXFE0kjlqGg73WgXSSDnnAG7GjSMkjJudip6lO+168D++a6TYvF0jWq4GhSKLMfTUqbEELAe04Ko396NdQ24jSvRaxIWgaWfLge3AHcpar/yXVbFoslt6hCwlESDvRUJENBTySlfdQYCoi3DcH0UaUVSUs/ki8DlX+LyCZMmK0/2uwBFsvwQhXiCSVutrI+zj9aDKZbvKaNIIPWcMfSBfmYxvyKiJwPfB7j9H2tiDwG3Kmq/8h1+xaLZfDSbuXac8OdUNA3asywHul3F7HrkUOfvKT48XLM3QXcJSIzMI7mfwf6M4KKxWKxAH7Dnb6vRwYDQsJVdjQkCAjmEZD2156Va0Day+z0a+GRt3x2XrbvL2FGd7Mw0VQsFoulIOhqPTLuKFvr41mfSzoTRe/ZCGt7mXjPyXXK5DHBgHjnskLaV/JhoHIaJpbkPOAN4GbgPlXdm+u2LRaLZSBItXZtK+2Xc3cU0kyjTF95IFUoU0XWvA569YYy+RjZ3Qr8EThEVd/LQ3sWi8UyZOkopP2X8cMvpEHpbJSZeeo2KarhYGGKZj7EbkqGrAIWi8ViKTD8QtpTy9kkI0oK0xQjJ2InIkeq6kve5pGdzTFb3zuLxWKx5INcjeyewIQEg7TEqD4Ua41psVgsljyQE7FT1Qrfaxt61mKxWCwDSs6FSESm5roNi8VisVi6Ih+jrjUi8riInCkiRd1Xt1gsFoulf8mH2M0C/gP8DNgiIjeLyOF5aNdisVgsFiAPYqeqa1X1KlWdBpwBlAOLRGRJrtu2WCwWiwXyGC7M42mgEpgCHJfnti0Wi8UyTMmLpaSIHCQivwI2A78Gngf2y0fbFovFYrHkIzbm68Bs4CHgbOAxVe2/+DYWi8VisXRDPqYx7wT+rKp1eWjLYrFYLJYO5HQaU0RCwE+B1ly2Y7FYLBZLV+RU7FQ1AewEwrlsx2KxWCyWrsiHgcpVwC0iMikPbVksFovF0oF8rNndjQn4/AURcfHljFDVSB7at1gsFsswJx9id2Ie2rBYLBaLpVNyLnaq+nSu27AMX+rqBOvIYrFYuiMffnaXdrZPVX+a6/YtQ5d4HEaNmkJR0UQmTnEYPzHO+IkJJkyMM35SnAmTEoyfEGfchARFxVYRLZbhTD6mMU9K254ITMMkde03sfMyKvwGOAEYD+wB7geuUNVWX73vARcCVcALwHxVXePtmw38xevfA8A3VNX19p0JnKmqn+qvPlv6xubNQVSF1tYQa1aGWLOy86Qao8cYERznieGESXHGe2I4YVKcUdUOAZt50WIZsuRjGnNuepmIXACM6c35RKQGWKuqkrYrhHFzOA1YCUzGCFYE+G/v2LOA7wEfA94DFgAPicjBquoA12EMau4AFgGfAh4QkWrgGmBOb/psyQ07dgQpK3NpaupepXbtCLFrR4i3Owk/Ho64jJ+QMKPDSe2jw/ETEkyYZASxtMyODi2WwUq+A0EnuQUTJ/Oq/jqhqjYBl/mK1onIXcB8X9l84FZVfR3apli3A8dgglTPBL6nqi0istjbBjNiXKCqW/qrv5a+c/jhMRoaNvDAg6vRsmls2RRm66YQWzaH2bo5xNbNYbZsCrN9awjXTf9vlEo8FmDDuggb1nVuIFw5wkkRwwmTzBRpUgzHjEsQGqhflMVi6ZKB+mkeDHR99+kfPgK8ldbur5IbqtooIiu98qe9uqeIyEZgLnCViJwGVKvq3Xnor6WHiEBlZZzx+0XZ733RjHUSCdi5PcSWTWG2bGoXQb8g7q0PdtvW3voge+uDrHg38/5AQBkzzojf+InJ5/bXEybGqaxykXx88y0WSwr5MFB5HJ9vHVAGHAb8MsftXogZsfkTxVYA9WlV6zBphwAuBn4LfBUzBfoC8BLwURGZD5wFNALfVdXlGdqcjzeSHDduLIsXd6iSFY2Nrb0+djjS2hxl47u13dYbUw5j9oODMuTbaGkJsmNHMTu2l7BjezHbtxezc0cJ27cXm/IdJSTiXU+Xuq6wbUuYbVs6DxhUVJRg7NhWxoxtZczYFsaM8V6PaWHs2Faqx7QSibjdvpe+EG+NZXW9LO3Ya5Y9WwJCrCVacPewfIzsnkvbbgQu7YlLgojcDHzR2wx4Zf7A0gtUdYGv/v8APwBOUNX1vnoNwIi001cBewFUdSNwuu88twM3ASUYo5YPAMcCdwFHp/dTVW8DbgM4/PCDdM6c3mUxWrx4Ob09djjy+JPvMX6/ffp8nlkpWwnM16UBANeFPbuCZnS42Zsu3RRm65YQW72yXTu6/zlFoyE2bChnw4byTuuMqk6uGZo1xAn+dcSJcUaP6ZsxzcZ3a5m8f03vTzAMsdcse0aUBFmzdG3B3cPyYaByTT+c43zgfEgxUKnKVFdErgDOA47PMPpaghlVPujVLcfc4zqYLYjIRzA59+ZjMqwv8dbyngEO7et7sgwuAgEYPcZh9BiHAw/JHNc8FhW2bTFrhls2GRHcuiV16rSluXuV2r0zxO6dId55K/P+UFjbrEhTrEsneuI4KUFZeW5HhxbLYCNnYudlPBBVjfvKzgEOAZ5R1Qdy0Ob1wOcwQrc6Q5XbgBtE5B8Ya8xrgbWkjT5FpAxjlHK6qqqIrAaOFJGRmLW8Vf3dd8vgJ1KkTKmJM6UmnnG/KjTUBzwxNKPDdDHMxpgmERc2ro+wcX3nxjQVIxxvJJgUwPbX2lRC0aggwRCEgkowBMGgEgwpoRB2TdEyJMnlyO5+4N9403oicjlwJcYI5DwRuUBV7+yvxkRkH8yaWwxYIu2/2HWqegCAqv7ZC0j9CO1+dp/w3A78/AS4V1VXeMe9JiL3Y0RuF2ZNz2LpESJQWeVSWdUzY5qtm1MFMRtjmob6IA2dGtN0PeUbCBjhCwYh5D0HQ5pRGIO+ss7rJkU17XVbXbztzur62vTVDYUgEPT309+XTP30H5/WT99r6285NMml2B0OfNu3/W3g66p6r4h8BrgUk9i1R6hqLRksOVV1XabyDPWuw/jTdVXnwgxllwCXZN1Ri6UXhEJ4I7AEh34wc53mJmkTvi2bQ2zzvd7qrSPGY72/Y7uu4MaEzOPToY9IquiGw0r16AnseyBMmxFj2swYNTNi7DMtZiPzDCJyKXYjVXUzgIjsjzEM+au370G8EZ/FYukZpWXK9Fkxps+KZdyfyZhm62afGG4ClTBOAhxHSCQExwEnITiOncNUFRJxM12cpG7PCFalLV6IKJOmxJk2M8a0GTFqZkbbxHDkKMdOBxcYuRS7JhEpV9VGzCjvbV/YLslx2xbLsKU7Y5pNb61iSs043MqKDvtUwXHAdYREmwCaZ78oJhxpE0sngbed9jpD3UTbMR3rpotuIpH2Onke19926jk79NlJq5vc30k//QLXHarta6fPPpW6b0SVwzSf+CVfT5wSt4EHBohcXvZngR+LyK0Y68h/+fbtB9hoJBZLngnu3EWguYVAa2tGsRMxU6mEFGP+Mvym6VyXFDFsbRFee3oXjYl9WLsqQu3qItauirBpQ7hTY6L6uiBvvlrKm6+WppSHIy5Ta+KpQjjDTItaC9rckkux+wGwEBOX8m3gBt++s+jof2exWHJIYG8DJW8vg5KMXjsWj0AAAhEIo4BSXgEHHFjH5P1Tr1u0VVhfG2bt6iJqV0VYsyrCWk8IW1syr5nGYwFWryhi9YqOQcvHjm+fEk2KYc3MGOPGJ+yUaD+QM7FT1bXA/iIySlV3p+2+DmM1abFY8oC0tFCyZClOaSn5idQ39CkqVmbNjjFrduqtzHVh+9YQa33it3Z1hNpVEbZv6zy6zvatYbZvDfPSc2Up5f2AQk8AACAASURBVKVlDjXT20eB02bFmDYjytSaOJGi4Tfy7i35cCpPFzpUtS5TXYvF0v9INEbJW2+jwRBaXAyt9gaZSwKBdovao45rTtnX2BCgdrURv6QY1q6OsG5tpNP1wuamIMuWlrBsaUlaO8rkqXFqZhrx848Kq0baKdF07FKpxTKUSSQofuddJBbHGZEeKc+Sb8orXA48pLWD4VAiAZs2hFm7qqhtFLh2dYQ1K4s69at0XWF9bYT1tRGeeSI1/NzIUQlqfGuCfgOZYPdumkMSK3YWy1DFdSlavorg3r0kRo0a6N5YuiAUgn2mxdlnWpw5vnTXqrBnd7BtKnTtSiOGa1dH2LwhTMe0noY9u0Ps2R3ijVdSDWQiRS5Ta/wiaISwZnpsyOdrzInYiUi1qu7MxbktFkt2RNbUEt62jUR19UB3xdJLRGDUaIdRo1v4wJEtKftaW8zIrk0IfdOinRnIxKIBVi0vZtXy4g77xk+Mt40C/aPCMeOGhoFMrkZ2a/DS5ojIE6p6Yo7asVgsGQht3ERk3XoSo0cPdFcsOaK4RNl3/yj77p8aes51YduWzAYyO7Z3biCTDE33wrOpBjJl5U6bZah/SnRqTYxw5+FZC45ciV3UZ4V5RI7asFgsGQju2Enx8pVm6tIGehx2BAIwYVKCCZMSfPj4VAOZhr2pBjK1q8206Pq1ERKJzMO3psYgby8p4e0lqQYywaBnIOMzjpk+K8pBB6SHGi4MciV29wHrRGQLUCoiKzJVUtV9c9S+xTIsCdTvpeTtd3Gqqhi2lgiWTqmodHn/oa28/9BUA5l43DOQSa4J+kaFDXszf48cR1i31liSPv1E6r6RIyfzwAN7mDMnc8DzgSAnYqeq3xaRvwAzgNsxWQQsFksOkeZmSpa8jVNWhoY7n66yWNIJh6Fmepya6XHm+spVYffOYIcp0bWrI2zZ2IWBzJ4ixowpLPeHXDqVvwC8ICITVPUPuWrHYrEkfeneQcMhtLhjdA6LpTeIJOOstnD4UakGMi0twvq1fgMZYxxTuyZCLCrMnFlYeTPy4VR+vZcR/FRgCrABeMQLEG2xWPpKIkHx28sgkcCtrBzo3liGCSUlyn7v65ibsaIoyCuLN1NUVDMwHeuEnIudiBwAPA44QC1QA/xKRE5W1bdz3b7FMqRxXYreW0GgoQHH+tJZCoBAAMaMKZy1uiT5MNW6EbgVmKqqxwJTgVuAX+ehbYtl6KJK0eq1hLfvtEJnsXRDPsTuUOCnqqoA3vMC4JA8tG2xDFnCGzcRXr+BxGgrdBZLd+RD7OoxU5d+aoC9eWjbYhmShLbvoGjFauNLNxTCW1gsOSYfsTH/ADwiIguAtcA04PvAPXlo22IZcgTq6il++10SVSOsL53FkiX5ELufAHFMMtekNeY9wPV5aNtiGVIEmpqML11FuXGOslgsWZEP1wMH+Jn3sFgsvURaoxQveRstiqBF1pfOYukJNnCexTIYiMcpXvoOuIpbWtp9fYvFkoIVO4ul0HFdit9bQaClBbeyYqB7Y7EMSmzyVsugJxQQJlaFiSZcWuNKNKEknCGSiFKVopVrCO3cZdP1WCx9wIqdZdAjAqPLU7/KCUd94jd4RTC8bgPhDRtJjLEJWC2WvpCPcGGXAXeq6tZct2WxJAkFhVAwSFmaHUdSBKMJpTWefC5MEQxt3UbxqjXER1tfOoulr+RjZPdR4EoR+RdwG7AwGU3FYsk3nYmg4/rFb+BFMLhnD8XvvEd81EjrS2ex9AM9EjsRqQBSVshVdXNXx6jqcSKyL/A14A4gISJ3Y0Z763rYX4slhdZEK3E3zopdawgQIBAIIEBAAoiIr8yMjERAEETEVybtZQGhpAhKi009VyGWgNaESzwB0YQSS0DcUQTfcckOiaSUAymvsyHQ2ETJkndwKisgZFcaLJb+IKtfkogchYmEMsNfDCjQ7d9OVV0B/EBELgU+DlwFXCoiTwA3quq/etpxy/Al4SbY01LP+vpNbG/aRTxewYb6OpLzBYqiqrSPycx22zcW2l+nlLULkiCYM6SP7Ewd11XijhJzXOIOxBIucUdxXJPwUgD1zp08V1IUk0LsyaDZRhCBcDRB9Vur0FAQV0qQZnOMYIQYjJiLQIAAiCfuSen2hN2cV9rEN0DAe3uC41YQc+LWFNsyrMj2b+OtwD8xI7Om3jQkIhHgs8DXgX2BP2HCh90hIv9PVb/Vm/NahgeqSn10L5sbtrGxfiuOupSGSxhTOprtAZeq4hED3UUAHFXiCSWWUGKOEku4xBwl4eAJcFI8FVVStoknGPXeWhAlURJBXaddcBVwaNtWoH0xIHkWTdn2rxa0byvh0v1ojO2lkom5vBQWS0GRrdhNAy7qzVqbiByMEbgvApuA24HTVbXe2/97YBVgxc7SgaZYM9uadrKubiOtToxIIExVcSXBQGGuYwVFCIaF4rRIXq56AphBBAHEcahatYlITImNGEEuA4G1WGMXyzAkW7F7CdgPeK8XbbwA/BX4hKo+n75TVbeJyG29OG8HROQ7wFnA+4HNqjozQ50vY6ZRJwBLgfNV9TVv33jgPkxaokXAWara5O37MCY331FeCDRLjog5MXY27aG2fgP1rY0EAgFGRMqpLBq8DtUBEYo7E8G4S/jdTQRam2iuriDoKo79hlks/Uq2Yvck8JA3CktxIVDVv3Rz7GRV3Z1eKCLiy3F3UZb9QERqgLWqmunv6WbgOmA28NUMxx6DSRx7OvA08N/AQhGZpap7gUuBV4GTgPuB84AbRKTIO+5sK3S5wXEd9rTWs6F+C9uadqCqVETKGVs2tB2pAyJUbtxG8c7dJCaOpMwbdblq1gQTjhJ3IOGakaAVwdwgAgGBQEDMsxjDpYDgrZlaBjvZit187/nbaeUKdCd2tUBlhvJdQL9mnVTVvwOIyDmdVPkG8ICqPubVux64ACN+fwBmAr9W1biIPAYc5B13FfCgqr7Vn/0d7qgqe6MNbGnczob6rSTcOCWhYqpLRvXIenEwE968g+I1G0lUV6UYyAREKAoJRW2/UDNtO1xFsCsxMg8hEPCeJVnft7/tuMz7u/u+1YYDzBxbREPUobHVpTnmYh2oBhdZiZ2qTutDGx2+RTJwd7KD8eXRU1UVkTe8coC3gI+JyCLgZOAxETkUmAcc0d3JRWQ+3h+DcePGsnjx8l51srGxtdfHDgZcdUm4DjEnhquusRYMlCNAM9CM26PzJVph2zuD724vCYdASxE64gBI9PAnIXT49SYtUFW9B2S+IUuYujrY21zbq3531h3zQlJ+8J4BaGqZpO33LFHTj0k57wDT2NjKS/9ZmVLmqvHPdNX8CbEYtgSEWEu04O5hPfWzG4fJSbdeVbd3Uze5DhfJsCY3HRiIK1GByZzup472kefPgN8Cr2HW7O4FnsEI2Cki8j9AArhcVV9MP7mq3oZxnOfwww/SOXP261UnFy9eTm+PLVTiTpxdzXtYV7+J3S31BEUYFSmnKNT3VDXb3nEYd0BhGqx0RqChmbJX38WtKEUjuTNHcVVJuMZCNO5CwnHZ445n3ORKRtZMJyDGqEaEjCOjoPcsATKOjILe66E+Eu/uNxl3lKaoQ0OrS1PMJZ4YvuI3oiTImqVrC+4elq2f3Ujgj5gRDoCKyELgK5nW4zySv2DxvQZwMQYvd2TbSRG5GWPNCV6mBhGp81VZoKoLsjhVA5Buo14FrAbwLES/7Gv3MmAxsBJ4ADMCHAv8S0RqbCSYrnHVpa51Lxv3bmFLw3YUpTRUMuTX4bpDWlopfXMlbmlxToUOjCBFgkKk7b9AkIYmYeqoCGPH2Jx4/UU4KFSVhqjysi+1xl0aoy6NrQ6NUTvlWQhkO7L7lfc8GyMMM4BfADcA52Q6QFW/CiAiy1S1T1nJVfV84HzvfDUYA5WqXpxqCXBYcsObTj0EI2QpiMj+wBeAD2KsOzeo6i5gl2ewMgbocnQ7XGmINrK1cQfr6zcRc+IUBYsYVVJFQKwbs8TilC5ZDQFBS6zYDFWKwwGKwwGqy0OoKs0xI34NrS6tcSt+A0G2YncysH/SNw5YISJfAZZ1d2Bfha4niEgI857CZlOKvT60elVux4zK/gA8C3wHKAb+kXaeAGbkeYGqtojIOmA/EZmKGdmFMQY2Fo/WRCs7mnazrn4TDdFGghJkRHEFoYANd9WG41CybC0Si+FWDV43CkvPEBHKioKUFQUZV2nW+RqjLo1Rh6aoSzRulS8f9OROlP6JdGpFICJvqOqh3uuVGY41J1TdtwftZ8PlGMvJJC3JLnntPSci52NEL+lnN89zO/DzHeAtVV3sHbdNRK4EXgFiwNetC4IJ27W7pY4N9ZvZ3rQLQagoKmdsmU1H0wFVipevJ7i7Aae6MKK9WAaGYEAYURJkRImZW44lklOeRgCdntlnWbIkW7F7AvijZ6BRC9QAvwQe76S+fzR3bW87lwlVraUTIy1VvRq4upvj78UYnnRV58YMZTcBN2XZzSFLV2G7hrqRQl+IrN1MeMtOnOrezL5bhjKRUIBRoQCjysx2S8ylwRv1Ndn1vn4jW7G7EONPt4r2UdpjmDBgHfA7mqvqH/rSQUthMNjCdhUS4U3bKV6zmUT1CJuXztItJZEAJZEAVJiA403eel9T1KElZpWvt2TrZ7cb4382AeN6sEFVt3RWX0SyijDbXXogy8BS6GG7XHV5bfNSXtm0klEaoSxSSmm4hLJwKWXhkrbt8kgpJaHiARHm0M46St5dR2J0pbHtt1h6QCAgVBQHqSgOAmESjtJoXRx6RY+sBzyB61TkfGykk3U6j6zTA1nyy2AI27V69zoWrlrEv1Y9zbamnaYwi8yIJaHidgEMl1AaLqUsUtImiEmhTNmOeMIZbt8uDRdnZVka2NtE6dLVOFUVNgGrpV8IdeLi0BQ1Lg6uXe/rlFyZyvUl4oolzwyGsF27mvfw79XPsHDVIt7bubpX52hJtNKSaO2+YhaUhks8cSxJGUWWeQJaToSqHY1GLN1yyoJFlAWLKQsWUe57LglErEuGpdd05uLQGHVpsSHNUsiJ2NkM5IOD5ngLO5p2UVu3keZ4C+FAmMqickIFsg7Xmojy9LqXWLhyES9ufB1HO/5trSqu5MjKI6msjtAcb6Ep1kxzvIXGeHPKdlO8JUMLvac53kJzvIWd/XCusoARwnKfIKZuF1MWaBdJsy9VOMuCxZQGIln9OQk3NAKFM1K39A8pLg60uzgkR33D3cUhJ2InImeo6t+811/srF4WGRMs/Uxq2K46ghKgPFLO2LKyge4aYNbhXt/yDgtXLuLJtc9lFKlIMMxxU4/g1FkncNSUw9j1rnQbLsxVl+Z4a5sANsVbaIo30xwzz00pwthMk1eeWr+lTeT6kyY3SpMbZXu8b+cRxAhfIPMosjRYxIeXNnDuXe/w0GUf4zOX3mj9IIcwnbk4NEVdGlqHn4tDtuHCLlPVn2Qov0RVf5bhkKuAv3mvOxznkU3GBEs/0HnYrsLxh6ut28AjKxfx6KrFbG3ckbHOoeMP4NRZc/nItKOpKCr37ene5TEgAcojpZRHSqGP64+O69CSiKaIY2OsmeZYM7F162mt28PeYqHZaaXRidLkPTe7rTQ6rTQ50bbnFjfWp774UZRGx7RBPD0ELJy5FM57AEIKp13zTwLHnw3HHtlv7VsKm+Hu4pDt37ofkFm0vocJnpyCqh7oe23X7waIlLBdboKiQKSgwnbtaak363Arn2LZzlUZ60ytnMi8WSdwyszjmVQ5Ps89zEwwEGwXTh9FqzZQ5EZIzBiZ9bkcdWn2iV+T00qTG+0gik1Oa2qd5D6vbnM3wvm11+C2h73AssCWSmHG1Em9efuWIUImF4cmL7LLUHRx6FLsfC4EAc/twL8gMAuI9qQxEalW1f5Y5rB0QqGH7YomYjyz7iUWrlrEfza8jpMhEM2IogpOnnEcp86aywFj9i0YI5muCG/cRlHtVuNL1wOCEqAiVEJFqKTPfUioQ7MT80SxXRhnPfI6xz/8Slu9DePLufOa4/jpPpP73KZlaNCZi0PS2GUouDh0dwf0uxBs9JULZu7oiu4aEJEyTLSVLwNFIhLFRDC5SFWbetxjSwcKPWyXqy5vbl3GwpWLeGLt8zTGOn7s4UCIY/c5gnkz53L0lA8QDuY2G0B/Etqxh5L31g+4L11IglSGSqhMCqcqo+97grF/ahe6lpmTee/SS7nwuJqB6aRlUJDJxSE56husLg7did00jLC9SXuCUzBxMXf4Aix3xU3AvsBpmFBj0zBrejcBX+1hfy0egyFs17q6TSxctYhHVy5ic2PmBBEHj9ufebPmctL0Y6lMWYcbHATrGylduprEyALzpVNlzN3/pPpvT7UVNb9vGht+9A3iFEZQAMvgIeniMNrn4tAUdWkYRC4OXYqdz4WgLwH9TsNkTEhaHawWkbeAd/twzmFLoYftqmut57HVz7Fw1SLe3p45P+/kygnMmzmXebPmMLlyQp572H8EmlspXbISp7wUwoUxTQyA6zLulgcY9c/n24oaD92XjVeeixYXgZ1PsfQBv4vDWDq6OBQq2Vpj3gncq6pP+8qOB76kqt/o5vBG2rMPJGnBJFK1ZEE0EWVXc11b2K5gIEhlpKxgwnbFnDjPrn+ZR1Yu4vn1r2Zch6ssKufk6ccyb9YJvH/sfgUz+uwtEo1TsmQlGg6hxZGB7k47jsOEG++n6on2qcuGDx3Ipku+nPNEsZbhSbqLg+sqawa4T5nI9u/oaXjJU328BPwV6E7srgTuEpHvA+uBfTAWnN2u9w1nCj1sl6qyZNsyHlm5iCfWPEdDhnW4UCDEMVMO59RZJ3D01MOJDKJ1uC5JOJQsXY04Dm5lAU29xhNMuu5PVD63pK2o/vhD2XzxWRAqjJG/ZegTCBTmH9lsxS5Ix/x1DpDxL62IxEmNjRkCPuOvAnwa+GOW7Q8r4m6Cp9a+UJBhuzbUb2bhqkUsXLmYTQ1bM9Y5aOxs5s2ay4nTj6GquDLPPcwxrkvxe7UEG5twRhVOXjqJxph87T2Uv9q+OrDnYx9i6wVnQLAwXE0sloEkW7F7BziTVHH6HJ1nKj+xL50a7iTcBCWBIKNKCuNmWt/awONrnmXhykW8tf29jHUmVYxj3qy5nDJzLlNHZJX0YvChStHqTUS27SExpnDy0gWaW5l8zZ2UvdXuq7j7k8ex7bxP2ZRCFotHtmJ3BfAvEfk4sALjY/cJYF6myv61PUvvGOiRXMyJ8/z6V1m4ahHPrn+FhJvoUKciUsaJ04/h1FlzOXjc+wa8z7kmvGEbReu2kBiTvdN4rgk0NDP1itsoWd4ejnbnF05ix9mnWKGzWHxkm8/uaRE5EjgPOAzjQvAhVX0rm+NFZDYwBxiDzzFdVX/Uw/5acoiqsnT7chaufIrH1jzL3mhjhzpBCXL01MM5ddZcjpnyQYpCBWSckUNC23ZTsmIDidFVBSMiwboGpl72e4rXtKeF3Hbux9l9xkcGsFcWS2GStb20J2zf6mkDIvIF4B7gLeAg7/lg4JmensuSGzbu3crClYt4dNUiNuzNnK7wgDH7cuqsEzh5xjFUFRfG9Gq+CNY1UvLOGs+XrjDWv0I76ph66S0UbWz3X9z6rc+w5+PHDGCvLJbCJWuxE5FjMFFQJqjqaSLyAaBMVbsTrcuAs1X1ryKyR1U/KCLnArN7321LX9kbbWxbh1uyLbPL44Tysd463Bxqqgo3tJSrLo2xVkKBIEEJEgqE+m1KNdDUQumbK3ArCseXLrxlJ1MvuYXItt0AaEDYcuGZ1J90xAD3zGIpXLL1s/siJuLJn4DjvGIFfoSZnuyKqbRnQEhyL7AB+H62HbX0nbgT5z8bX+eRlU/x7LqXiWdYhysLl3LS9GOYN2suh4x/X8EEjU4nmojREGvEVReRKqqKK2lNRGlNRKmPNqK4mBlzbXsWAoQCAUKBkCeKXQujtMYofXMFWhRBiwpjujayfhtTL72F8C6T1UBDQTZ9/0s0HHvIAPfMYilssv2rehlwsqq+KiJne2VvAwdkcWwdMMJ73iYi+wO7gMJIoDbEUVXe2bGCR1Yu4rHVz1Af7ejLH5QgH55yGPNmzuXYfY6gOFQ0AD3tnoTrsDfaQEIdSkPFzK6ewZiy0by8aT0Hj98vrW6CuJMwz655jiZitCRaaY0bUWxJtBphVNdbh2sXxkDcYdTb62iOOlBVQchJEAgEBlT8i1ZtZOplvye01/g0upEwGy87h6Yj3jdgfbJYBgvZit1EVX3Ve530n0tg/O+64wngdOBujBP6E0AceLQH/bT0kM0N21i4cjELVy1iff2mjHXeVz2TebPmcvKM4xhVUjim9H7MFGUzrYlWwsEwU0dMZELFWCoi5V1OVYYCoawzPSQ8MTTi6BBPRNHX3yEu5bSMLSGaiBJz40RjMdTV1NwfanQyIEGCEiDoTaUG+1kYS96tZcoVtxJsMuFo3eIIG67+Os0Hz+q3NiyWoUy2YrdaRD6sqv/xlX0YyBz80IeqnuvbvMo7pgL4Q9a9tGRFQ7SRJ9c+zyMrF/HG1ncy1hlXVs28WXOZN3Mu00ZOyXMPs6cp1kxzogUUJlaOY1LF+JzFAE0KY3EIUIV3V0IzMG3/DnUd1yHhJnDUJeE4JDRBwnGIJqK0OjFiToyYE6c51oKraXEYvL+JRhDbhTEg0uX7Kn1zJVOuuYNAq8lX55SXsP5H82ndv6Z/LoDFMgzIVuyuBf6fiPwaCIvIRcCFwPyeNKaqCvy5Z120dEXCTfDCBrMO98z6l4k58Q51ysIlnDDtaE6ddQKHTTigwNfhGnAVRpdWse/oaYwuHZnfdD9r1sPajTAuc3qkYCDYLkzddMtxHSOO6pBwHBx1iDsJookoUTdOzIkSTcRpScRx1EG13atBVRGE0a+tYsp19xGIm/XVxIgy1v/0m0Sn28SrFktPyNbP7kERaQK+A6wDTgDOVdXHuztWRILAxZh0PpMxefHuAa5XzRAx2NItqsq7O1fxyMqneGz1s+xpre9QJygBPjT5MObNmsvx+xxBcah4AHraPQk3wd5oIwnXoSxcwuzqmYwtq6YkPAD93bwN3lsFY0b3iy9dUhgj0K0wuuqScBIk1BNI1yHwxH+oXPAXxDEjxNjoSt695hyaJpaD95knVxkFEAmYKdS2UWOgYLJhWCwDTadiJyI3qOp3vdcneMLWrbhl4OeYQNLX0Z7P7iKMg/lFvTjfsGVr4/a2dbjauo0Z68yunsG8mXP56IzjGF1aOJE+/Ljq0hBtIupEiQTD1FRNZlx5dbfrcDll1x548x2oHjUgeekCEiASirQHm33oCbj297RlyZw0jsjN13LwpPFtwuio6603GnGMOjFiiRhRxzya4y0kXAeEtlGjKrhaGMHELZZ80tXI7mvAd73XDwK9jej7JeAoVV2bLBCRp4AXsGLXLY2xZp5c+zwLVy7itS1LM9YZV1bNx2Yez7yZc5kxap889zA7VJWmeAvN8RYCIkyqGM/EynFUFVcO/LRqQyO8+haMqIRQAfjS3f9PuP7W9u2ayXDztTDWiFRSGLPBVbdtpJhwzcjx9R1hKiKFkR7KYskXXf2ya0XkZ8BSzDrdF0i1QwNAVf/STRsukG4OuJmOWRQsHo46vLjxDZ6qfYGna18k6sQ61CkNl3BCzVHMm3UCH5hwYMFOV7UmojREG1GU6tJRzK6ezqiSqvyuw3VFSyu8sgRKiqG4AFwu7v4b/O7e9u19p8PvfgQjexe1JiABAsFAyvUOBqKUhAvgvVoseaS7kd1PgM9iVhx+mqGOAt2J3Y3AL0XkYlWNikgxsAC4oRf9HdK8u2MVt732F+55/f+oi3dchwtIgCMnHcK8WXOZs8+HBmZdKwsSboL61gYcdagoKueAsfsypmxU4a0bxuPw+tuAQFnpwPZFFW7+oxG7JAfNhl9fBRUFlDPPYhmkdCp2nl/dRwG8MF/Tsj2piKwkNZ9dDfB1EdkOjMX459UC1/e8y0OXZ9e/zI0v3dWhfN9R05g36wQ+NvM4qktHDUDPusdVl73RRmJOnEgwxPSRU806XFGB3qgdB5Ysg6YWGD3APoauCzfcAfc93F72wYPgl5dDacnA9ctiGUJ0ZaCyXVXHepv/6OF5r+19l3KLZx26ADgHKAYeA85T1Z0icjTG+X00cIuqXu477ofAOFX9n1z17Yz3ncp3Hr2aqBOjumQkp8yay6mz5jJzVE2umuwT/nW4oASYWDGOSZXjGVFcMfDrcF2R9KXbucdYXg4kjgPX3gQPP9Fedszh8PNLoEBClFksQ4GupjFDIlKkqlHMVOa5XdRNQVUL2WH8h8AngSMxYcvuwiSlPQW4BWM08zzwmog8oKqvi8h+wNlATiPtjiwZwS9Ovoz69XDUYbMYUVyYRgStiVYaY8246jK2bDT7j5nBqJKqrCOWDDiramH9Zhib2ZcubyQScMUN8Piz7WUnHg0/vgjCBbKmabEMEbq6Oz0OLBOR1UCJiDyWqZKqntxdIyLyQYxYTsEEgL5LVV/pRX/7g/nAj1R1jde37wOrRKQGmAk85q0tvgTMFJE3gDuAb6tqU647d8ERX+HRvUsIBlpy3VSPiDtx9saacNwElUUVHDB2X6pLRxbeOlx3bNwCy9cYp/GBzEsXjcEPfw7PvtxedtqJcPkFA+L6YLEMdboSuy9hRnQzgOMxo50eIyKfAv4XMxX6BjAdeFpEzlLVnk6P9gkRGYHJwvBaskxVV4vIXtpz7c0TkWcwo7hrgAuAd1X1qZ60tXz5GubM+Xyv+rl7TxOBEmfgLSxVSaiDqhKQAEWhIiLBMMECm6Ksq2uhqiqLta14wrgZhEIDKnQljsO1a9bzgYb2/07/N2YUN23cgn7z8i6O7B/qYi5VFcUQsaPHbMn6O2YBCvN6dWWgEseIFCIyTlWvZA1XqQAAIABJREFU6WUbVwGfUdWFyQIROQWzbpZXsaPdVzDd1LHO2/c1jPXolRgn+BZMwtojReQK4ERgB3CBqm5NP7mIzMcLoRYOh6mr693oTF3FaQngptj45A8Fs66FIJJMgSNEcYkSHZA+dYXjuFlcazXrY4RMCPMBurYVjsOC2nUc3Nze3zvHVPPbcWPz1i9HhbqmODR3TPFkyUx23zFLkkK8XmLCVXayU+QAVc0cUdjsn+cXsU7q7AFGq7ZHxRWRALBbVfNqBiciVcAe4FBVfdNXXo9JMPtQWv3HgN9gsjT8ADgJI4jHq+pZXbV1+OEH6auvPtxVlU559IkllM5soTySvyxILfFWGuNNqCrjyscwpXICI0tGDIp1uMWLlzNnzn6dV2hugRdeg0hkYK0b99TDBVeaadQk3/oyfPWMvHZj8aYoc46YCuPHdl/ZAmTxHbOk0JfrJVLzmqoe3s9d6jY25gv4IqeIyG5V9du+30f3kVXWYUZE/jW/jwDre9DPfkFV60RkPXAY8CaAiEzHvIe3/HW9bOo7VPWf3rreS6rqeFOcF+a56zkh7sSpjzXiui6VReW8f+xsqktHUlSg+ex6RSwGry2FQHBghW7HLjj/Cli7ob3s4vlw5mkD1yeLZRjRndilL2x0t52JH2MyJvwdWIOJjfkZ4CtZ9bD/uQ34gYgswlhj/hz4t6rWJiuIyATMSO5or2g1cLGIlADzgFV57XE/4rgODTHjD1ccKmbWqBrGlVVTFhlgp+pc4Djw5jJojcKoAfSl27wNvnk5bPJmvgMBuOwC+ORJA9cni2WY0Z3Ypc9xdrfd8QSq/ycimzF+bR/EWGOeqKovZNvJfmYBMBJ4BSjCWJ1+Ka3OLcCVqrrT234Q+DiwBSPYX8xPV/sHVaUx1kSLEyUoQaZUTmBCxRhGFFUOXODlXKMK76yA3XUD60tXuxHOvxy27zLbwaBxLTj52IHrk8UyDMnpgoyIhDDBpH8zgOKWgpdW6OL/3959h0dVZg8c/55MCgkJvYTeu0AQ7F1Wxfaz4KprWSuuva0dFbCuHbHr4rpiXV3L6lpXQVfdxbUgKqKIgEjv6XXO74/3XnIzJqSQmUkm5/M88yRzy9x33kzm3Ld7j5qOObKac07zHs1GUVkx+aUFINC1dWd2aNuN9q3axr+XZywsWuKGGcRzLN2iJXDe9S7gguv9eNtVsFdUh2saY6pRW7BLFZFrAs9bRTzfZt9lVS0XkWtU9fYGp9DUS2lFGbml+YQ1TLu0LEZ1HUan1u1JDbWg2Th+XgGLlrpVAuJVcv3me7hgCvjDC9Jbuem/dh4dn/QY08LVFuz+i+uB6Jsb8fy/dbjGbBHZR1U/qG/iTN2UhyvIK8mnLFxGeko6Qzr0o0tmJzJSmtY4l5hYux6+/t6tS5cUp/GAn30Nl97oeoECZLZ2EzqPHhaf9Bhjth3sVHXfRrjGUio7qCwlsLSPqla3koKpA1Ulr7SA4vISkpOS6dU2m26ZXWiTlpW47XC12ZLrel52aAfJcaqq/fgzuOJWN0MKQLs2cP8NMHRAfNJjjAGi3GbnycHNnDLAe/iU6pcNMttQWFZEfmkBSZJE19ad6dW2G+1atWkZ7XDbUlAI/5vvlsOJ18wg730Mk+90c16CK10+eCP07x2f9Bhjtop6sFPV/aJ9jURXUl5Kfmk+FRqmQ3o7crJH0DGjXctqh9sWVVeiSw65trF4eP19uOFet1wPQLcu8NBN0LNbfNJjjKki2r0xTwXGAJ+q6tPRvFaiKQ9XsLFoM+VaQUZyK4Z0GkDn1h1bZjscuCBSXFL5yM2D3Hw312VBK2hd7qoM4+HFN+BPD1U+79MDHrzJTTZtjGkSohbsvLkkrwa+BSaJSLaq3hWt6yUSQQhrmN5tu9MtqwtZqZktox1O1a0e7ge0/ELYkgf5Ba6zx9ZRneqWwElLddWWJUC7OM368uRLMOMvlc8H9YUHbozvIHZjzK9Es2T3e+BQVZ0tIuNxEyxbsKuDtORU9um/R9NeAHV7VFRUBrSiYlc625LnuulXVLigJrgB2Kmprg2uY/ttDCOoiGHiParwyNPw5+crt40YDDOmQtumuQ6hMS1ZNINdF1Wd7f3+PmCNF3UkSPMPdKpQ4gW0klIXyHLzYEs+FBeD//4Er5SW4qoh4zVcoD5U4Z6Z8MyrldvGjoS7r4XWCTjtmjEJIJrBbuttuKqqt9KBSTRlZS6YFZe4qsYtea6kllfoLRMEoG4NudQUyGgFbTLjmuTtUlHh2udefrty2+5j4faroVUCTaBtTIKJZrCLXN08M3K187qscm6agHC4MqD51Y55+a6DSGlZZbWjJLkSWmoqdGwX35XAo6G8HKZOh7cC8yPsvzvcfJkrnRpjmqxoBrubIp43aKVzE0OlpZXVjgVe55DcfPf71oK61zkkNQVat4a2LWR8X0kpXHM7fDC3ctuh+8N1F8ZvALsxps6iFuy2Y2VzE00VFVVLaVu78BdUDoZWdZ1D0lJdKa1Th8QrpdVHUTFcdjPMnVe57beHwOV/aB5tjMaYmMygYmJNtbKU5nfhz813ga2omK2ltGDnkDaZLsCZqvIL4OIb3Lp4vt9PhAtOadk3AMY0MxbsmrPy8spqx8IiF9C25LngFg50x09OrhyXltk6fultbjZvgQumwneBtXrPPgnOOLb5Brr1myApE9rY8AjTsliwa+qCXfiLSypLaHkF3mTDAqj78vXHpLVvJl34m7L1G+Hc6+Cnnyu3XXomnHBE/NK0PcorYMNG6J4Nm5Igo4XOxGNaLAt2TVFFBXy90AW2/ALX21HVq3ZMdkEtI93uzqNl1Vq3uvjyVe65CEw+D448KL7paii/bXbEYOjdAz74Id4pMibmLNg1RaVlsHKzmwprmzOHmEb380o4ZzKsWe+eh5Jg2qUwYZ/4pquhNm2BJHFjAdu1jXdqjImbqAQ7EQkTmMmwJqpqPSJq0irNtbGZ2PlxKZx3HWzY7J6nJMOtV8K+u8Y1WQ1SUeGqYrt0gpFDIM0GvJuWLVolu70Cv48DzsbNi7kE6A9cDDwSpWsbU38LFsEFU1wHH3A3G3dOhl3HxDddDVFcAptzYdhA6NvT2m+NIUrBTlW3DiAXkfuBw1R1sbfpPRF5H3gRmBGN6xtTL19+CxdPg4Ii97x1Btx7PeSMiG+6GmJzrmvf3XWMqwI3xgCxabMbACyP2LYCV8IzJr7+84UbMF5S6p63zYL7b3ClouYkHIZ1G900baOHQas4LWJrTBMVi/qNz4E7RaQVgPfzT8CXMbi2MTWb8x+49MbKQNexPTx6a/MLdCWlsHY9DOwDO422QGdMNWJRspsEvAZsEpG1QBdgGfB/Mbi2MdV7cw5MvQcqwu55dmd46Cbo1T2uyaq33Hy38sROo11nFGNMtaIe7FT1RxHZAdgV6IGrwvyvqsZhxU1jgJfeglsfrFyCqHd3ePBGyO4S33TVh6rrbdkmE3bJsUHixtQiJuPsVLVCRD4BslV1VSyuaUy1nnoFps+sfD6gDzxwI3RqRp05ysrc8Ih+vWBIf5vT1Jg6iHqbnYhkishMoAj40dt2pIhMifa1jdlKFR57tmqgGz4QHrmleQW6/AI3PGLHETB8kAU6Y+ooFh1U7gK6AnsAXk8A/gccF4NrG+MC3Yy/wCPPVG4bMwIevBnatYlfuupDFdZvcGPm9hgH3brGO0XGNCuxqMY8DBiuqltERAFUdYWINLOeAKZZCofhtofh729Wbtt1DNx5TfPptVhe7lYr6N0Nhg1yq1gYY+olFv81gqvCrNwgkgnkx+DapiUrr4Ab7oU3Zldu2283uPlytzpEc1BQ6JZsGj0MemTbPKnGNFAsqjE/Bq6O2HYBMLuaY41pHKVlcPVtVQPdwfu6uS6bS6DbsBnCCnuOg57dLNAZsx1iUbK7FHhfRE4CMkXkayAFGB+Da5uWqLgYLr/VzY7iO3oCXHVO85gn0l97rltXGDHILelkjNkuUf/PV9XlwA7AVcA1wA3AGFVd0djXEpE/i8i3IlIuIn+uZn9IRO4QkXUikicifxeRToH9e4jIDyKyQURuijj3KhG5p7HTbBpZfiFcOLVqoDvpSLj63OYR6IqKXaAbMRhyhlugM6aRxGLowcmqWqKqf1fVO1X1BVUtEpETo3C5+biS5D9q2H8VcASwC9DT2zYrsP8h4I/AIOBEEdnRew9DgJOBa6OQZtNYtuS5RVe/+LZy21knwEWnN48qwE1boLQUdhsLfXo2jzQb00zE4lb3gRq239fYF1LVGar6NpBbwyFnAbep6k+qugW4ApggIn29/QOBd1R1IzAXGCgiAvwZuEBVCxo7zaaRbNgEf7jGLdXju/h0OOt3TT9oVFTAmnXQLssNK2hvi6wa09hi1Ruz6gYXXMpjcO3gNdsCvXETUwOgqotFJBcYBSzFlQwPEZEPgZ2BacD5wHeq+n4drnEWLqDStWsX5sz5vkFpzS9T5uSlQoHNqFYX5avWUXj9FDJWrgRARfjhvLNZdcBBsKaJ56GqC3ZpnSE/Ff6zNOqXzM8vbvBns6WyPKufpphfUQt2IlKGW608JCKlEbtDwIPRunYN/NHDWyK2bw7sOwOYDlwP3I4bMnEesIuIXAf8BlgHnK+qqyMvoKqPAo8CjBs3Svfdd0iDEjrnza/YN6MUMls36PxmLxyGsnI3LVZZuetZGfm8vAxKy6GwkOLbH6PV2nXu3FASMuVihhyyHw3L/RjanOve65gR0KlDzC47Z873NPSz2VJZntVPU8yvaJbsfoMr1b0BHBzYHgZWq+qias+qI6/Nb+tq56qaWcsp3hLURNYRtcOr9lTVb4EDAtd4B7gMN4n1fsD+uIB4FxCNNsfYUHUDlasNIsHtNQSYsrJfn1NjUAps39b5wXP9lQjqaOvQ8ORkuPUKN5auKQuH3STOHdrBqGGQ3kwGtxvTjEUt2KnqBwAiMiAakz+r6tPA0/U4frOI/AzsCMzz0tYfV6qbH3m8iJwOrFPV10XkCmCuN6H1h8DFjfEefmXtenhzDt2+WAJSCJJUxwARCDA1lYYifyaatFS4czLstmO8U7JtpWWwcZObgHpQP5vb0pgYiUWb3bki8qaqfuJvEJHdgYNUtVEngxaRVFynmxCg3kKxYVX1q1EfBa4UkdnABuA24G1VXRrxOt2AK3HzeQIsBi4TkXTgELwJrRvdj8vg1MuafvVbLKSmQEoypHg/U1NcyS0lBVKTITll6zGrM9qSfdr/wdAmvuhqXr4Ldrb2nDExF4tgdwYuqAR9DbwANPbKB+8A+wSenwp8AOzrPf8T0B43EXUa8C5wUjWv8xBwvaqu956/gpvjcxXwE3BCI6fbieXMHqFQzQElcvvW371A4/9ep3Mi9/06WP3q2FCoXj0oF66pILtrEy4h+WvPZWW6QNc6I94pMqbFiUWwywAKI7YVArW1sdWbqu5by/4KXBvcZbUcd2Q1553mPaKnS0c4+WhWrcmlW7q4BTmrDQ7VBZF6BJjkUPMYYJ0Itq491xMG97dJnI2Jk1j85y0CDgIC087zG1zVoAnq3QOevJvv3/yKbhlFLbc3ZqLIL4SiItfbsrstyWNMPMUi2N0KPC8iDwE/4GYnORs4MwbXNib2VF0nlPR0N0g8q9ErMYwx9RT1YKeqL4lIEW5w9mG4wdsnqOob0b62MTFXXuHa53p1g2EDXRWyMSbuYtKAoKpvUrUa05jEU1gEeQW29pwxTVBUgp2IZPszjGxrRXJVXRmN6xsTcxs2u84/e4yFtm1qP94YE1PRKtn9QOUUXL/gpg0LEm9bE+4vbkwdVHjVlrb2nDFNWrSC3YjA7/2idA1j4qu4GHLzYfggW5LHmCYuKsHOW7DV/31ZNK5hTFxtznX1E7vuaEvyGNMMRKvN7vq6HKeqN0Tj+sZETTgM6zZClw6wwxBoZZM4G9McRKsac6/A7wLsDawGlgF9gGzcNF7GNB8lpbBpMwwZAP172yw0xjQj0arGDC6TczfwPnCrqqq37WrAZsI1zceWPNcZZZcxMV17zhjTOGIxzu73QLYf6Dx34Ep6f4zB9Y1pOH/tufZtYfRwW3vOmGYqFvUwRcAOEdtGAsUxuLYxDVda5tYY7NfLrVZggc6YZisWJbsHgbdE5BHcVGF9gbOA+2JwbWMaJi/ftdGNGwVdO8c7NcaY7RSLuTFvFZFfgJOB3wIrgCtV9cloX9uYelOFDZvcihO29pwxCSNWc2POAmbF4lrGNJitPWdMworJf7OI9AeOB7qr6vkiMhhIUdVvY3F9Y2pla88Zk9Ci3kFFRA4AvgJ2xfXMBOgM3BntaxtTK7/aUnBrz1mgMyYhxaJk9yfgt6r6lohs8rZ9AewYg2sbU7PyChfoembb2nPGJLhYBLsBqvqW97sCqGqRiNg3i4mfwiLIL4AdBkOv7jaJszEJLhbj7JaLSJVxdiIyGjcMwZjY27jZlep2Hwu9e1igM6YFiEWwmwG8JCInASERmQg8BdwTg2s3XxVh155kGk9FBaxZBx3b2yKrxrQwsRhn95iICHAlbrHWacB0bziCqU4oBKEk1w0eDSx96/2Skuy6xSeH3M9QyEontSkudvNbDhsIfXtZfhnTwkQ12IlICBgLPKGqj0bzWgklNQX22dWV7MrLoazcjQEr9R5Fxe5RWASFxVBa6pUC/S9wdV/mfjBMDgTHlmjzFvdzt7G29pwxLVRUg52qVojIbCAzmtdJWCKuh2BKCpBe83GqLhj6QbGs3E11VVhUNTCWlPgvHPgRDIohV2oMJUhQ9Nee69wBRtrac8a0ZLHojbkAt4bd0hhcq2USgdRU99iWcDgQFL3A6AfFQi8o5he4fa7fLC4iqlu7LVh1mhxq2kHRX3tucH8Y0MfWnjOmhYtFsJsFvCIid+AWbw37O1T1kxhc3/iSkiAtzT22JRgUS8uqD4q5+a6K1Q+GkUEx2K4Y60Djrz23cw507hjbaxtjmqRYBLvp3s/IDimK67Bimpq6BsWKiqolxdJSKC6prDotKoFN+S54+sFQxFW7hiKqTpOTt7/TiKpbe65tFuSMsCV5jDFbxaI3ptUfJaqQV5VZW0wJdrLxfxYVe6XEIhcUcyOCIrjgFwpVrTqtKSiWlsHGTa7KclC/pl3FaoyJuWj3xhyIW6h1nqouiea1TBPm9watraRVHigh+qXFrb1Ovc42W/JB/aAIoFCeCbkFtvacMaZGUQt2InI08DyuqrJURI5W1TeidT2TAOoSFKsbjjFvFew+3NaeM8bUKJpVjNcC1wBZwBTv96gRkcEi8qKIrBCRPBH5VkTOjDgmQ0QeF5FNIrJZRGaKSHpg/1EislxE1ojIORHnPiwiF0XzPZg68IdjZKS7GVA6d3TPLdAZY7YhmsGuH3CXqhYAdwMDo3gtgPbAbGAnoA3wB+BOr4TpuxcY6j0GA8O8tPkD4B8DDgd2Bm4Ska7evn1x1bH3Rfk9GGOMiYJottmFVDUMoKplIlLLILDto6pzgbmBTR+JyLvAPri5OdOBk4DDVHUNgIhcB7wmIpfgAmSZqs7z9i0C+ohILnA/bpmiMMYYY5qdaAa7VBEJVl22iniOqt4SrYuLSAawG64KFWAIrt/g54HDvsBNTTIY+BqoEJGxwEagL/AjcBPwjKp+F620GmOMiS7RKM2sLyJzCExhXA1V1f2jdO0Q8DegC7C/V7LcC/gQSFLvTYtIElAB7KWqH3nH3Aqkej9XAA8CewO34BacXQxcqKp51Vz3LOAsgK5du4x97rmGzXWdn19MZqaNEasry6/6sfyqP8uz+tme/Npvv4M+V9VxjZyk6AW7aBORE4FH/OeqmultTwGeBnoCB6vqFm97DvAl0F5VN3vb2uNKcaNVdX7E66cCnwKn4drwdlfVU0TkFnc5nbyt9I0bN0o/++y1Br23OXO+Z999hzTo3JbI8qt+LL/qz/KsfrYnv0T6RiXYNdsB36r6tKpm+g8AEWkFvIwr0R3oBzrP90AxrnTmGwMUAT9Uc4lrgX+q6pfAaMCf2uxD7zxjjDHNRLMNdpFEJBN4E1cFebCq5gf3q2oRbtHYG0Ski4h0AW4AnlTV4ojXGgUc4e0HV3V5gIgkAwfj2vKMMcY0E822GjOSiJwCPIErqQV7TT6lqmd7x2Tgelb6wxH+DpzvBUL/dULAf4CL/YmqvUD6DK5n55fA8aq6upb0rMNNfN0QnYD1DTy3JbL8qh/Lr/qzPKuf7cmvPqra6FMhJUywSyQi8lk06qwTleVX/Vh+1Z/lWf00xfxKmGpMY4wxpiYW7IwxxiQ8C3ZN06PxTkAzY/lVP5Zf9Wd5Vj9NLr+szc4YY0zCs5KdMcaYhGfBzhhjTMKzYJcARERqP8oYY1ouC3bNnIi0Btp5v1vQqwcR6Sgioy3f6kZEOonI/sEFj03D2Geu4bwJ/OvNgl0zJiJXAHnA6+Bmp45vipoPEbkKWAc8h5vtwWyDiFwKrMX1shsR5+Q0SyLSS0Smiki2qqoFvLoTkeEi8pKIdFbVcEPyzoJdMyUifYBRwBnACBE51Ntuf9NaiEg2UILLvy7A7+OboqZNRNoAa4CRuFVCjhGRdvFNVfPiLRC9DPgDcAHYzWldichpwGzgSCqHNNQ72NnQg2ZCREKqWuH9LsF/FBG5HzhEVfvHLYFNmIgk+XeDkV8wXonlGmBXVbUJvqn6Watm36nAZOA8VX0npglrhvzPnIj0xxUu9sfdXF2sqp9V95k0TuD/tjeQC/TALbg93lt/NElVw9t+lUpWCmji/JKaqlaISKqInIxb2cHfL8B03ErwV3rbQnFJbBPjV3V4/zDpQO/AvpC3725gE3CZt6pFixXxWWsjIjeLSJUFllX1CVwJ5WQR6RGHZDZ5wdqVQCBb7t1MfQGsBs6L2G/4Vd75gewXbw3Sn3HNDg9F7K8TC3ZNnP8HFZGLgQ24FRvCgf2KW4LoT8D1IpLpfVm1+PaAwIr0U4CfgGdF5HURyfHyyP/8nwuciVukt8UKfNauBVYCfYCP/f2Bm6jrcSuA7GM3Vr/m3Vx1EZEjRWSkt63M+/kZ8DYwRESOBuusAr+6Me0tImeKyN4i0sX/XKpqHnAH0ENEzvLOq/Pnz4JdEyciO4nIt8Ak3IK0R/n/OD7vS/1Z4DvgAf/U2Ka0aRKRP+DWJtwfuAxXHfKqiAwP/BO9C7wHTPOWc2oRJMB7PkREFgAnAONU9SRVLfGP92+ivKWv3gVOBwbFJfFNmIhMBpbi2uc+EpEbRKRf4JC3gW+A00Uk3Up3VW5MpwNfA+OBWcAMEekVOPQ74B7gVu+zWOcbewt2TUgNf7TDgF7ABFX9j4h097p/9xaRrdWZqroOt9jsiSIyqqE9lpqryDu8wHvfGZirqt+p6ieqegKuk8WlXkcV35m4gPh/MUlwnHntcup9yfh5NRAoAB5V1YUiMlRELhKR8YEvaz+fJwP9gcPErRNpAK9t7nBgf1U9GFdduSNws3+Mqv6MW2g6E/DX2mwx/6vV8arNZwJDgB1U9Xe4GpfOuP9LwN1wATNxPYOn+6fX5RoW7JqAQFtJdXd4t+Cq4M4RkYeBz4CrcNVLN0f0vvwAeAV4eBuvl1Cqa9MUkTSvU0AqkA2s8o71v6gvAw7B9cZERJJVdTlwLzA1IggmlIj8ShGRh3BfHuBKty8Ax4nIG8C/cNWV04H3RKSXqpZ7gXI18AiuFDg65m8kzqTmXs8HAZ1U9b9eyeMp4K/AjiJyYuC494H/AkeKSN+WNBShhrwrxpV2b/T+F1HVf+I6pWzxzvOrOlcCNwHnenlXpxt7C3ZNQKCt5AwR+aeIPCoiR4hIlleNdB8uwHUA9sSVPqYCuwIXBV5ni3fcriJyRIzfRlzU1KbpfSGX4joEnCQirfwehqr6HvAD4H/5+P9El+JKN79L1LaoQH5dirsJOB3IFpG2qlqMC3ibgCIgR1WPBnbBDT24xXsZP2DeBgwHJraU6t+ItqVMr5khOE7zOyBVRPoEbjbfx5XkJonXCcr7X/0Hbqznhd62hL45DdxohUUkPVgj4P2vPu5VkfvHtwdKgTXejUMwf14CXgYeq+v1Ldg1ASIyWEQ+AP4IPAMk4+5cJgCo6kzcHfRJqvqT96U0C9crrpeIhEQkSUQ64+7SfwSWxOGtxFxNbZqBrvN34Ep3Z3rH+0HseWC0iKSoapmIdBWRJ3F590ZNXe+bOxGZICI/4rq/74mrRuvqffmiqp8DU4Dfq+p67yahEHgc2EtEUr38aituyMsvwExVzY/PO4qtQNvS1bj3fjcwT0RO9GoS1uFupE4KnLMB+AjXsWyHwPZPcOM8DxSRnjF7E3ESuNGaAszHtcsF9/slOL9XdA6QBXwfeSOgqkVABTDeq3Go9UbBgl2ceXc3E4FvVHW4qj6tqqcD+bg/tu9FVS0VkRTYeifUA/hZVSu8D1IucLeqDlbV+TF+K/GyrTbNVqqaC0zDVfnuHAhiA4B3A519tgDTvbz7PvZvI2YmAPepao6qLgS+x90wDQkc84WqFnil42Jv2w7Ay97nDlzb3gxVHaiq38Uu+fEnInsCR+Hakn6DazaYDJyhqt/iahP2FJHdA6ctAsbh/q/915mMC3YTVfWXGCU/brx2uSdweRYGjpLqh6/4vc0PBD5W1Y3e+ePFTY+IiJwDpAOD/WrPWqmqPeL4wDX4HwwM95639n4+jQtcNZ13K/A5rjEXICne7yXG+eZPiJAGfImrYnsY12X+Hdxd9x1AyDvuH95xDwAzgPXAkcHXaokPYD9c1duu1exL8z6fd+O+rA+Md3rj8RmrZvt1wP8itt3lfe52xPVQfQnXYzXD278/rhdmp8A5oXi/xyjm3a/eG2588Jm4Nt5dcOPmTqgpH4DXcE01OwDzgK+AgQ3NOyvZRVltDafqShrvqOoC73mBt2soborB5otTAAAgAElEQVScYBdxf269lcDuwPGq+o13Xr0GWDYH3nuu9jOqquqVPEpwwSuyTXMKsBuuahjgNFxvVYA2wFhVfcV/rei9i9ipawcH/8PkPf030BPo6O1L8n5m4tqFl+C+bPbXFjJjSnUdxiLacLcAK0SkS2DbY7j2pWNUdRFu3GtH4BMReRP4J/Cqqq73T9AErCqXbUxMoK5W4BlV/UpV51I5fGVwNa+zA64n9aO4KuC/qepo9WY5akjeWbCLIu/L+FdfpJFf4JF/OHEDUdvg7mRQD6677WZgkqruo6qLttErrFkTNxWQqmvMzhGRDpHHaGWHk79QfZvmUlzni2RV3aCqLwMXquqpqroskfLOzy/v9+TA9l8FQP/z5O1rBczF3RhsvWlS1wb3AfBbVT1QVZcnUn5ti1a2LZ0tIv/wtlUEAt463Gw8OYFzFuLaoXYWN3buU1yb1B9xX+q9VPXBGL6NuNBaJiZQ1cLA5yg4fKVNxEt1xw07mA30VNVbvNdt8GfQ5saMMu8O+Ubc6gTfq+rT2zjWn0fvCmAfVT1U3IS7k4F/q+o/qjs+mumPJ6/DzSPAXsBRqvpRNcf4eZasrlt8inrtcCIyG3c3PT1wXJWfsX1H0eXl1724btwrgFvUNeTXdt4HwEeqOtn7MtFg3vgBM9HyqyYi0h24HReskoDbVfUu/zPmHfMJLrjdom7cHF4b3b+A7uqmt4p83XrN5dgcichg3PAngKO9m4Dqjgt5NxDXAMcBv1PVBSIyGldlngq0CeTtduddi7hTiwdxvSP3xI2R64PrVfSAiNzlV39so9ppAPCGuClxluHuIrdWIbWELx8RuRFYiLtJ6FldoAvyv4QCge5WXOn4X952re5novC+JOYBZbi74dOBmX4niUD1pATO8UsqnwKHgrszj8ybQM1CS9EL9393PC7o/UHctFXlIpLmHXMjsAfesBZvWz9cO1NB5AtCYjY1VKMftUxM4H0G/f/DW3CddqaKyGJcm3o7Vd2sqj9736PSKHmnTaAxM1EeuKB0f+D5nbgea/7zfXDzWF4KpHnbkiNeoz3uCyuMG3Q6LrCvRXSkwDVKh3G92/xtQ/Aa+wPbkiKe98G1M63EVcENivd7iULeSOT79rZfAcwKPB8N/B03SDzV21ZTR4Cp3mc1taV8xuqQxwO830fgxh4+7D1PChx3Ka6T2Bzc0Ix84Kx4pz9eeeb9TAOuBP4DvIHrKPYSbgqwn3A3rpHnzsWN67w5mmm0kl3jGo6bAghxs+xv7VbrFcM/AP6G6325M7gSiYj0EZEDvUN74RZjnaiqu6pbBsS/u0mou+tt1L9/C/wFOE1E+ovIHOAp3DyDt4lIV9g6OLW/iJwvIlm4gc+bSNA2zYh2zJ4i0jawOxvXzuFX7X6F+6x1wXXOQV21kZ9fwXPvUdXLVLU00T5jkcTZ5mfCy+PF3tNFuPbfg0RkFy/v/ZLcdFxb8Qu4CQ2Gq+qjv37FxLCNmihUtzYNlODaKGuamOBW77VC4gblF+Nqb3qo6mRvX3T+Z+N9R5BID9zsHd8Env8N15uoTWBbNq6u/6LAtieANd7voYjXTLg77eB7wt0c3ABcjqu397fv4P0T5OPuoPvjFqpdhOt56ZeMb8V90XTa1nWa86Oa/Hre+wyNCmz/I26WjpGBbZ1wvQSfALJaSn5tIx+DpbJdgS51yXdcjcGzwFuBfem1XSORHhF517W29wzsROUwqlbez0m4TmOpgbztFau8S5i73iZiHpArIvt4z5/G1fsP9Q9QN6fg+7iB5L4ZwBIR6a2BBVq94xPuTtt/TyJyPm6szSBc9+OZInKhd9gS4GTgN6p6t7peljNxX/QTcN28wc00U4TruYX3ugmVd4H8ugLXlpQE7K2q8wN32//DtQvvF3j/64EFuPwt9I77VX5FXidRqSuVdRKRF3Hd2cfUcryfH7/gbhi6ee1P/8bNd1lFo7UtNUFaWZvwLvCaiDwvIif4+6o5pbaJCfzP6HKvtB31vLNg17jW4YYGDPWqnF7FTW46WaouU7EEKAg0dqfgSoEb/QMS8YtH3KwmD3i//xY3Hu63qvo7VZ0EXI03Lk7deMN3gLleNW669zIbcd3l/ech3Ji6dYkW5CLy6zTcwPmrVfW3qrpZRLr771VVP8S1i0zAzTzh+wEXBP15CH+VXy2FiNyEmzEmH1fqeLuG44KdeMS7Af0C97m7B9f2dHLkeYnyuauOuJlO/oGbgOD3uLGGN3ufy615JpWLIvvDqZK9Ksu7cZOvv+ntr7ImZyzyzoJdI1K3uOBHwAFUrvN1Jq4t7zoR2dHriXkS8KZ6a4Wp6lyv9JLo8wtubdPEzWDynKq+F6ijLwIWSuWUaIXe/0FYVYtEZACuqvhxdfM1oqq5qjpTVdcn4JfNcFygAhfI/gb099rdXgGeFpF/iMgl3jF34dpKbheR//N6aE4BXvI+m4meX8DWdrnIJZ96AdfgZiU6VVVLRGS0uEVWg8EtKZgvqqoisiOu2ngTbgaPU7zzE+77cxs3QENxHeeuVTec4ALciuEzRKSbl0/+jYH/Wu1oQhMTJNwfqwm4G9eD63AR6aBuZpRLcLN7PIa7M/qvqs6IYxrjJQM3PyCqOltVH/e2+18unYFVGlicVkQ6isi+IjILN93Xu5rAnQAiZABjYesK17NxKzV8jZtw4Abv510icqC62SWuws2KciGuo9NsVZ0a+6THR6ATT4WIDBO35FOGuvkTbwGOF5FdvarIv+B6Uv5ZKlcUD0vlTEXdvZcNA8ep6zD2k1dSSbgqS6k6MUF6xO7+QLaq5gY6otyFa0O/MfAaPUTkFRHpp26s4b9pKhMT1NaoZ48GNeYehOt2OyVi+3ACjbskeIeAavKlP/AJsGd1eYD74j4j4pw03M3CAwQ6FLSEvAvk197e8z64jjyjI477K4HOE962LLxOKS0hvyI+R5m4QBYGVkTkyRrcoPtLcNN5HYUbi/kQXkcyXKllBTBmW9dJxAeuU9MLuOVzHqByCEY33Pi5//OeJ3s/j8ZNQN/Ze34Arjf1qZH5Fu+8s5JddLwD/Bk4RUTO8arfABaq6hqpZu69FsJv0xweuUPcrPvtcEMMEJEDReQgdXeQD6nqeaq6toXlnZ9fw7y76WXAX1T1KxFJDrT55gKF3ja/3TJPVfNaSn7570/cSgLLce1rpwBrRWSYd0wermPYIap6j1ZOIfd33NRf/jRrD+FqG5KJkMj5KCIDcWPe8nGTqu+OmwjjUFVdBTyHqwpGvUkccG2gP+IthIybGqwQ1+tyK/VE+z1siwW7KPD+ri/husqPBe4TN42VP+9gQlV/1JVWtmkeGLgB8A3GlYY7i5uP8HXcDCio15srEauOtkWrtgH397at936Wq2s36oX7on7W2xY5+0lC55dX5eh34nkcOAK3OsPvcF/CXajsiQqupDzHO94fL7cZaEvlrB7luM5SK1tYJ549ge9U9TR1nXeOxrW3TRW3Vt/DQFcRuT5wThFuKMFCcO3suFlnvmtqeWfBLopUdTZwDq7oX1bb8S2E36Z5lIi0Ud06IfHvgD/gqkCWq2qqqr4QPDHed4ZxUiW/AMQtNLu/iMzADS34MDKvWpAReDdFwDRV3VlV/+c9X4Br99wRKm+WAjedxd5N1zm4EvMm/0VVdZaqrki0z1xkxx1vmx+UuuGm+/LzagmuilyBy7x8vR64VkTuF5FjcUOBvgE2B3pivqCqa5pa3lmwizJ1q2aX135ky+CV0i7Gre91kbc5hKva/Dtufb7zoPp/zJamhvzagLs56I4bWO7PPNGk7qRjJANvvJxXzRv83GTg2oF7e/v9qs52IrK3iDyL6/Q0R1XviHXC40Fdx53WIjIhsM0PSguBEhHZJ7Dta1wtywEi0llVZ+F6mJfjOkG9pqrHq2qBNvEli35VJ21MDLwDtAbuFJHNuAG7Y7VyQH2SdwfepP95YiiYX5twVZaT/J2B/GpSd9Ix4k/ksIeqfhzs/q6qq8RNi9YZKmfax7VJdQdW4YYSrPX2J9yUfJHEjYu7F8gUkZGq+q1UriiwCNfeeZSI/Nv7TBWIyELcROF+ye1J4EmpugpEk1/RwUp2JuYi2jRzcFUhIW98VItql6uLiPwaA8wSbyyi5dfWTjwjoErpzS/dvY/raOGXasT7gv67ql6qCd7pSdzEBPd7v48BDsNN3PA6boA8VLZVfoNrIx5F1UHzS3ET1FepoVI3r6/fIarJfwatZGfiRlVni8hH7ler6q1NdfmViF/Q9aGux6nf6ek99SZwDtQKFAFFIpKqgYmutXIpqES/WQhOTLAGV6r7DNdr8isROUJVXw2Uemfhhh/c6fX2/Ra4A3gLN6i+iub0+bOSnYkra9OsH8uvagU78bQF8HoPgpvXcq+aTmxOX9YNFJyYYKWqfqhuZqIFwP24oJbslXqT1M3de5237xDceMWPVPXC5t6sYCuVG2OaPRE5CNeB5z+qGpzRozNuJZHVcUtcHIlIf9zY1StU9aNgu6SItMeNk7tbVf9Uzbkh3IoFBd7zZt2maSU7Y0wi8CdyOE1EzhZvVWxgg6qujus0VfFVZSKHYLDyhlpMBa4UNwXa4SJyul8qVtUKr4NKQrRpttQPgDEmgUR04hmHm/nDJnKoZiKH4BAVVX0QNyl7LvAqUKpuCZ7gayRE3lk1pjEmoXg9Va3Tk8ebKeZLYCbwmKpu8QJekrftROBmTfAJw603pjEmoajNVlSFupliLsa1aaYDN6qqilvpIRMYpm7FjOBYxIRjJTtjjElwXknuKOBO7/G6qv4c2N/kB4VvLwt2xhjTQojIfrhqy+7AEapa1tx7WdaVBTtjjGlBWmqbpgU7Y4wxCc+GHhhjjEl4FuyMMcYkPAt2xhhjEp4FO2OMMQnPgp0xxpiEZ8HOmHoQkTkiUiIi+SKyRUS+FJGJ8U5XUyAi+4qIenkTfPSPd9qMsWBnTP3dqKqZQEfgWeB5ERkc5zTFlL9SejUqVDUz4vFTA14/FJyw2JjtZcHOmAbyBuU+CISAkQAicouI/OSVaBZ7cxLi7UsVkUdFZK2I5IrIDyJyjLevr4i8LSKbRWSTiHwuIkMC504SkW8CpckDA/umish73rXXeo9pwbSKyKEissBL1+sico+IzAns7ygiM0VkuYisE5G/iUjXwP6lInK9iMwWkQKg3qVZEblNRJZ4afhRRC4I7BvolQpPE5GFQCHQUURSRORaL682i8i/RWTH+l7bGAt2xjSQt+7XeUAZ8JW3eQGwJ5AFTAJu9RYWBTgV2Ak38W4bYLx3PMAtwM9AV6ATcBpuHTJE5CzgStw0T+2BycBLIjIwkJy9vfO7A4cD14jIHt75A4CXgBuBdsA9uKVw/PchwCuAAjsAfYA84JmItzwJuBQ3efCr9cgq3zfA7l7enA3cISLjI475nfde2gCbgJuBQ4GDcCXpp4C3/BXJjakrC3bG1N9kEdkM/AIcAUz0Z41X1adUdaW3vtr7wD9xQQ2gFBcohotIsqouV9UFgX3ZQH9v0cz5qrrG23chcIOqfqWqYVV9A5gNHB9I0w+q+rCqlqvqXGAebl03cAFkrqo+6+1/j6rBaqz3OE9Vt6hqIXAFsL+I9Awc95iqfum9t6Ia8ibklcD8xyv+DlWdpaqrvPP/BbwVyBvfVFVdq6oluOB7PvBHVV3i5csjwAbg4Bqub0y1bIkfY+rvZlW9qbodInIhrgTUExDckip+CekpXMntHmCQiLwHXOEFysuB64DXRKQ18CJwtarmA/1wi5HOCFwqGRdsfasiklKAK0EB9ACWRexfBvTyfu8HpAFrIprJioHegessre49R6hQ1XbV7fCqdM8IXDcDt5J2UPAaXXH596aIBOc1TMXlrzF1ZiU7YxqJV214G/AHoJP3pf8aLujhlapuU9VxuKrCQuBxb986Vb1QVQcCewD74kpX4ALT6araLvDIVNVz6pi0Fd71gnoHfl+GC44dIq6RrqqfBI5r8BIwIrIPrkpyEtDRy5s38PKmhmusBYqAfSPSlaGqdzY0LaZlsmBnTONpA1TgSisqIocSqG4Tkf1FZKzXk7EIF2DKvX3HiUg/r/1sC65a05+V/h5gqojkiJMuInuKyNA6putZYBcROdbr5bgvcGRg/2e4as97RaSjl57OInL8r1+qwSLz5nDgwG2d4C0ieh9wl9fuiIhkisgEEcluxLSZFsCCnTGN521gFvApsB44Bng5sL+rt38TrtqxD64UCDAG+ADIB74FvsAtsomqPgbcDvzFO/dnXJVnTd3/q1DVxcBvgWm4QHqZl44Sb38YF/ySgM9FJA+YiytdNpY3cEH3c1zAO5K6dXKZDLwJvC4iucAPwFnYd5epJ1vix5gWSESeBfJU9ax4p8WYWLC7I2NaABE5XETai0iyiByBGyf3bLzTZUysWG9MY1qGfXDVoK1w1aBnq+rs+CbJmNixakxjjDEJz6oxjTHGJDwLdsYYYxKeBTtjjDEJz4KdMcaYhGfBzhhjTMKzYGeMMSbhWbAzxhiT8CzYGWOMSXgW7IwxxiQ8C3bGGGMSngU7Y4wxCc+CnTHGmIRnwc4YY0zCs2BnjDEm4VmwM8YYk/As2BljjEl4FuyMMcYkPAt2xhhjEp4FO2OMMQnPgp0xxpiEZ8HOGGNMwrNgZ4wxJuFZsDPGGJPwLNgZY4xJeBbsjDHGJLzkeCfAmESQmppyUJcunaaUl5dnYzeRjSWcnJy8eu3a9dNKS8vejndiTPMmqhrvNBjTrKWmphw0YECf+5944s7SnJwRxWlpqfZP1QhKSkpl3rxvW5166mWpixcvO98CntkeFuyM2U49e3b75O9/f6jtLruMKYp3WhLR3Llfpk+ceM6WX35ZtXu802KaL6tuMWY7lZeXZ+fkjCiOdzoSVU7OiGKvetiYBrNgZ8z2S7Kqy+jx8ta+q8x2sQ+QMcaYhGfBzpg42XvvYwdde+2dXetyrEjfsW+//UFmtNNUm513PmLIFVfc0i3e6TCmvmzogTFRkJExdIz/e2lpmQCkpqZsreosLFz45Ycf/m1RLNLy+uvvZR155KTB5eU/fV7bsa+88k7WHXc8kj1//sLWoHTt2rnssMP233TddReu6dixfUUs0mtMNFjJzpgoKCxc+KX/mDhxwoYjjjhgY3BbvNNXnRkz/tLx+OPPHzR+/O65Cxb865u8vAXzXn995qK8vILQ//73VXq802fM9rBgZ0ycRFYJfv/94tSDDz6lf+fOY0ZlZY3I2XHHQ4euXr0uFHneypVrkseMOWTocced16esrAyAWbNeajdixAHDsrJG5PTvv9eIhx56qgPA0qW/pBxzzDmDKirCZGQMHZORMXTMffc90THyNbdsyU2aPPnOXueff8qqG27445pevbqVAwwdOrB05szbl0+YsG9+de/hmGPO6ZudPW5U69bDxgwYsPeIhx9+uoO/b926DaGDDz6lf7t2I3OysobnDBq0z4i33pqTCfDxx5+ljx176JCsrOE5bdvukDNmzCFD163b8Kv3akxjsWpMY5qAvLyCpPHjTxyy3367bVm48P1v27TJrPjgg7mtI3t5zp//Xdrhh58x6LjjDttw++3XrAJ4+eW321xwwZS+zz13348HHLBX/ocffppxxBGTBvft26P04IP3y3/xxYcWHXnkpMHbKlG+994nmfn5BaFTTz1mY33Svcce4/Lvv/+G5Z06ta94/PEX2p933nX9dtppVOHYsSOLp027N7uoqDhp2bKP52dlZYa/+eb7NL8q9/zzr+8zfvweW+bOffX7ioqwfPTR/zLS0tKsR6uJGivZGdMEPP/8621LSkrk8cdv/7ljx/YVKSkp/OY3exa0b9827B8zZ85/M8ePP2HIVVedu9IPdAAzZvyly5lnHr9mwoR980OhEPvtt1vhUUcdtOGJJ178VQmuJmvWrE8G6Nu3V2l90n3JJWesz87uXJGcnMxZZ/1u05Ah/YreffffWeDaKDdt2pI8f/7CVqrKqFHDSoYOHVgKkJKSosuXr0xdvHhZalpaqo4fv0dBmzaZ4W1fzZiGs2BnTBOwdOny1F69upempKTUeMxjjz3XddCgfsVnnnlcldLX8uWr0h544MnsrKwROf7jxRff6LR69bqaXyxC166dyv101PWciooKLr54Wve+fffcIStreE5W1oichQt/Sl+3bmMywNSpl6zee+9d8k4//fJ+HTvm5Bx99B/6Ll++KhngySfvWhIOq+y997FDe/TYZeRFF03t7lfJGhMNFuyMaQL69u1V+ssvq1LLy8trPObhh29eEg6HmTDhlAFFRcXib+/Ro2vJZZdNWpmX9+08/1FQ8N2XH3zwtx8BQqGkWqsHx4/fPT8zs3XFX//6YofajvU9+uizHZ555tVOL7zwwI+bN389Ly/v23lDh/YvUlUBaNMmM3zffdNWLFr0wbfz57/9zerV61IvvHBKT3BtgS+88ODSNWs+n//yy4/8+Mwzr3Z64IEn61wSNaa+LNgZ0wQce+yhW1JSkvXMM6/qtWHDplB5eTnvvfdx602btmz9H83Kah1+//1nF1VUVMj48ScMys3NTwK46KLT1z7yyLNd33prTmZ5eTnFxSXy739/mvHhh3MzAHr0yC6rqAizcOGPNZba2rZtE77ppj8uv//+J7OnTZveZeXKNckAP/zwU+pZZ13d0+9YEpSbmxcKhUKand2lPBwOM336zI4LF/60tdfmM8+82vaLL75pVV5eTtu2meG0tNRwcrLrJnDffU90XLr0lxSADh3al4dCIfx9xkSDBTtjmoA2bTLD77779PcrVqxOHTRo3x3atx+dc8UVt/b0x+j5MjLS9e23Zy1u375t+T77HDt4/fqNoaOPnpA7Y8bUZVde+aeeHTrk5GRnjxt9ySU39srNzQ8BjBo1rOSkk45at9tuRw/LyhqR88ADT1ZbervootM3PPPMjMVvv/1h2yFD9tshK2t4ziGHnDaodev08M475xRGHn/uub/fkJMzvGDo0P1HZmfvNHrBgh/Tx40bubXX5o8/Lk078shJA7OyRozp23fPka1apYWnT7/+F4DZs/+TtdNOhw/LyBg6Zo89jh521FEHbTj77BM3NG6uGlPJVj0wZjtlZ3deunr1Z+vjnY5Elp09rtPq1ev6xjsdpvmykp0xxpiEZ8HOGGNMwrNgZ4wxJuFZsDPGGJPwLNgZY4xJeBbsjDHGJDwLdsYYYxKeBTtjjDEJz4KdMcaYhGfBzhhTxaOPPtt+7NjDhmRmDhuTnNx/bF3Pmzjx7L7HHXden2imzZiGsmBnjKmiY8d2FWeddcK6m2667Od4p8WYxmLTjBtjqpg48eBcgNdffy8rct/HH3+WfuGFU3r/8MOS9KSkJPr37138zjuzFt177186vfrqux0AMjKGdgDIzf3mS1vJwDQV9kk0Jg5E+tS5erCxqC77fHtf4/zzr+8zfvweW+bOffX7ioqwfPTR/zLS0tL0ppsuW/Pddz+mJyeH9PnnH1jWGOk1pjFZNaYxps5SUlJ0+fKVqYsXL0tNS0vV8eP3KGjTJjMc73QZUxsLdsaYOnvyybuWhMMqe+997NAePXYZedFFU7uXlZXFO1nG1MqqMY2Jg8aoUoyHoUMHlr7wwoNLAT79dF76oYeeNqhfv14lF198xoakJLt3Nk2XfTqNMVWUl5dTWFgk/irphYVFUlhYJOFwmPvue6Lj0qW/pAB06NC+PBQK4XdC6dq1U9myZSvSKioq4ph6Y6pnwc4YU8WDD87q2Lr1sB0nTjx7UEVFmNath+3YuvWwHRctWpI6e/Z/snba6fBhGRlDx+yxx9HDjjrqoA1nn33iBoBzzz15XWFhcVL79qNzsrJG5JSXl8f7rRizlahqvNNgTLOWnd156erVn62PdzoSWXb2uE6rV6/rG+90mObLSnbGGGMSngU7Y4wxCc+CnTHGmIRnwc4YY0zCs2BnjDEm4VmwM8YYk/As2BljjEl4FuyMMcYkPAt2xhhjEp4FO2NMVB133Hl9Jk48u2+802FaNgt2xpitzjlnco+BA/cZkZk5bEyXLjuOOv748/usWbM+FO90GbO9LNgZY7YKhUL89a93/bRhw1fz5s17c8HKlWtTTzjhwr7xTpcx28vWszPGbHX//Tes8H/v3r1r+bnnnrx20qQr+/nbJk48u29FRVhatUoN//Ofs9unp7cKX375WSsvv/wPWyfCnj59Zse77vpzt02bNqf85jd7bVZVkpNDNuO8iSsLdsbEgUzrOzbW19QpS+u9YOy//vVR1uDB/YuC2956a077mTNvX/z00/cue+qpl9udccaV/Y844oDcwYP7l7711pzMq666rc+zz97342GH7Z/7yCPPdLz44hv6HHHEARsb750YU39WjWmMqdYTT7zQ7tln/9H53nunLA9u33XXMbknnnjkllAoxCmnHLM5K6t1xaeffpXhznmx44QJ+2466qiDclNSUjj//FM2jBo1tDA+78CYShbsjDG/8vjjz7e/6KJpfZ977r4f99xzpyrBqmvXTmXB5xkZrcK5uflJACtXrk3t06d7SXB/r15VnxsTD1aNaUwcNKRKMVbuvffxjtdff3evF154cNGBB+5dUJ9zu3XrXLps2cq04Lbly1em9evXq7hxU2lM/VjJzhiz1U033ddlypR7ev3jH3/+ob6BDuDUU4/Z8NZbc9q/+uq7WWVlZTz44KwO8+cvzIhGWo2pDyvZGWO2uu66u3qFQiE9+OBThwS3FxYu/LIu5x988H75t9xyxc/nnntt3y1bcpPHj99z86GH7r8pOqk1pu5E1XoEG7M9srM7L129+rP1tR9pGio7e1yn1avX9Y13OkzzZdWYxhhjEp4FO2OMMQnPgp0xxpiEZ8HOGGNMwrNgZ4wxJuFZsDPGGJPwLNgZY4Qc4WwAAANOSURBVIxJeBbsjDHGJDwLdsaYRnfFFbd023nnI4bUfqQxsWHBzhhTxQUXTOnRo8cuIzMzh43p0GH06AkTft9/0aKlqfFOlzHbw4KdMaaK008/dsP8+W8tyM//7stlyz75umfPbqXHHntu/+qOLSkplVinz5iGsImgjTFVjBkzYutyPKpKUlISS5YsbwUwceLZfcvKyiU1NUXfffff7Q47bPzGp5++9+fnnnut7eTJd/RctWpt6i675OT169fL1rAzTYoFO2PiQfqOjfk1te5r6D388NMdLr/85t75+YWhUCik06Zd8ou/780357R/8MGbljz33H1Li4pKkhYsWJT6+99fMmD69ClLJ006fuNrr73X5sQTLxowcqStUG6aDqvGNMb8ytlnn7gxL2/BvGXLPvnqj388c+Xo0cOK/H1jx+6QP2nS8ZuSk5PJymod/utfX+wwcuTQgnPPPXljSkoKRx89IfeAA/baHM/0GxPJgp0xpka9e3cvv/DC09Yfe+x5A9esWR8C6NWre2nwmF9+WZ0aua1v3x5WjWmaFKvGNCYe6lGlGG9lZeVSVFSc9PPPK1IAkpKkyiKYPXp0LXvvvU/Sg9uWLl2RFss0GlMbK9kZY7aqqKjgllvu77xixepkgMWLf06ZNOmq3t27dy3NyRleXN05p5xyzMb58xe2fuSRZzqUlZXxyivvZP3rXx+1i23Kjdk2C3bGmCrefvvDtiNHHjQiPX3ImN12O2pYenqr8DvvzPohJSWl2uNHjBhc8sQTdy6+7baHurVtO3LM9Okzux5//OG2crtpUkRVaz/KGFOj7OzOS1ev/sy+3KMoO3tcp9Wr1/WNdzpM82UlO2OMMQnPgp0xxpiEZ8HOGGNMwrNgZ4wxJuFZsDNm+4VtQuTo8fI2HO90mObNgp0x2yk5OXn1vHnftop3OhLVvHnftkpOTl4d73SY5s2CnTHbae3a9dNOPfWy1Llzv0y3El7jKSkplblzv0w/9dTLUteuXT8t3ukxzZuNszOmEaSmphzUpUunKeXl5dnYTWRjCScnJ69eu3b9tNLSsrfjnRjTvFmwM8YYk/DsDtQYY0zCs2BnjDEm4VmwM8YYk/As2BljjEl4FuyMMcYkvP8HAw2lPoFQjcAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sel = [1,2,3,4]\n",
    "temp = bi.loc['1',['mean','std']].iloc[sel,:]\n",
    "plt.plot(sel,temp['mean'],linewidth = 3,label='1st',zorder=4,color='blue')\n",
    "plt.fill_between(sel, temp['mean'] + temp['std'], temp['mean'] - temp['std'],\n",
    "                 zorder=1,alpha=.2)\n",
    "\n",
    "sel = [0,1,2]\n",
    "temp = bi.loc['2',['mean','std']].iloc[sel,:]\n",
    "plt.plot(sel,temp['mean'],linewidth = 3,label='2nd',zorder=4,color='green')\n",
    "plt.fill_between(sel, temp['mean'] + temp['std'], temp['mean'] - temp['std'],\n",
    "                 zorder=1,color='green',alpha=.2)\n",
    "\n",
    "sel = [0,1,2]\n",
    "temp = bi.loc['3',['mean','std']].iloc[sel,:]\n",
    "plt.plot(sel,temp['mean'],linewidth = 3,label='3rd',zorder=4,color='red')\n",
    "plt.fill_between(sel, temp['mean'] + temp['std'], temp['mean'] - temp['std'],\n",
    "                 zorder=1,color='red',alpha=.2)\n",
    "\n",
    "plt.axhline(0,color='black',zorder=3)\n",
    "yticks = list(np.arange(-.2,.31,.1))\n",
    "plt.yticks(yticks, [('+' if y > 0 else '') + '{0:.0%}'.format(y) for y in yticks], fontsize=13)\n",
    "plt.ylabel('Effect on\\nPredicted Probability of Survival',fontsize=13)\n",
    "xticks=['(0,25]','(25,50]','(50,75]','(75,100]','(100,512]']\n",
    "plt.xticks(range(len(xticks)),xticks,fontsize=13,rotation=30)\n",
    "plt.xlabel('Passenger Fare', fontsize=13)\n",
    "plt.title('Interaction Effect\\nPassenger Fare vs. Ticket Class', fontsize=15)\n",
    "plt.gca().set_facecolor('lightyellow')\n",
    "plt.grid(zorder=2)\n",
    "\n",
    "legend = plt.legend(title='Ticket Class',bbox_to_anchor=(0.5, -.4), loc = 'upper center',fontsize=13, title_fontsize= 13)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('lightyellow')\n",
    "frame.set_edgecolor('black')\n",
    "\n",
    "plt.savefig('fare_class.png',bbox_inches='tight',dpi=300)"
   ]
  }
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